{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed5bb203-fbb3-4483-a08f-97f879c021a5",
   "metadata": {},
   "source": [
    "### 张量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "891349de-f869-424e-9c4b-cec52929795b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d17717a-8270-4bb2-ac6b-33d37889c0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([3, 2], dtype=torch.int32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建tensor\n",
    "torch.tensor([3,2], dtype=torch.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e703ca6-9b18-473b-91df-4797ed4e9cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6, 2], dtype=torch.int32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(np.array([6, 2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad96983e-46bd-44a6-b16c-9a2f53b21916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.4277, 0.6296, 0.0544],\n",
       "        [0.3646, 0.9708, 0.0128]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 快速创建tensor的方法. 和numpy中的routines方法一样. \n",
    "# ones, zeros, full, eye, random.randn, random.normal...arange...random.rand, random.random\n",
    "# 创建一个0到1之间的随机数组成的tensor\n",
    "torch.rand(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd294fab-a657-463e-8bdd-b30882b319a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5409, 0.9883, 0.7469],\n",
       "        [0.1139, 0.2652, 0.2687]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2238176-ef90-452c-b974-a7cc36952130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.2427, -0.4533, -0.9001],\n",
       "         [ 0.9915, -0.7172,  0.5667]],\n",
       "\n",
       "        [[ 1.1597, -1.1167, -0.3045],\n",
       "         [-0.7350,  0.5964,  0.4275]]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#标准正态分布\n",
    "torch.randn(2,2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80db59d3-dbaf-453f-95ba-b49f0cff7f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.],\n",
       "        [0., 0., 0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成0或1的张量\n",
    "torch.zeros(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0f478baa-f6b7-4e69-81f4-7b0b85d04718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ones(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ef7d6a6-fd1a-41f2-8ae3-5fb011ba6d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tensor的shape\n",
    "x = torch.ones(2,2,3)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5558de55-8fe8-4682-b28c-f40634ea8c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 2, 3])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#size方法获取\n",
    "x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e3cabba-e8ae-48c0-b06e-c58c377f37d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#size方法中插入shape的索引\n",
    "x.size(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd9706a-6919-417b-bfa9-6d721a508875",
   "metadata": {},
   "source": [
    "### tensor的基础数据类型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17136660-be91-48d8-b5a1-e55eb2d34210",
   "metadata": {},
   "source": [
    "32位浮点数: torch.float32\n",
    "64位浮点数: torch.float64\n",
    "32整数: torch.int32\n",
    "16整数: torch.int16\n",
    "64整数: torch.int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "25dfb7f8-482a-406a-8c09-9e2c02b550fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6., 2.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#创建时指定\n",
    "torch.tensor([6, 2], dtype=torch.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47640253-3a93-468a-baa6-2487d3d00afe",
   "metadata": {},
   "source": [
    "### 张量运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "344063e4-67fd-49d3-bbf7-c84abf552770",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tensor运算规则和numpy的ndarray很像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "37ed63cc-4675-4a6e-a62f-23f865f87e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 3.],\n",
       "        [3., 3.],\n",
       "        [3., 3.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#和单个数字运算(以下的运算后原来的值不变)\n",
    "t =  torch.ones(3,2)\n",
    "t + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5baada9d-0db1-416a-bf08-5bf4631548ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 3.],\n",
       "        [3., 3.],\n",
       "        [3., 3.]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.add(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ff1ea9ea-c9f4-43e6-b777-e9ae088708f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 3.],\n",
       "        [3., 3.],\n",
       "        [3., 3.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.add(t, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b680b128-d1e8-43e1-9ff9-46adcaa7e66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 3.],\n",
       "        [3., 3.],\n",
       "        [3., 3.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#运算后原来的改变\n",
    "t.add_(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "79617039-6d9f-4098-874d-fa6e57ed97ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3., 3.],\n",
       "        [3., 3.],\n",
       "        [3., 3.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b1c6154c-09a0-4ca1-bcfe-fdbd16b098b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#改变形状\n",
    "t = torch.tensor([[3, 4],[8, 0], [9, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1120be65-f9ff-4189-a6c9-378fca92cd06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[3, 4, 8],\n",
       "        [0, 9, 6]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.reshape(2,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6baa0a8c-ab05-4150-95c8-ee985cdd30ba",
   "metadata": {},
   "source": [
    "### 实现线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "15ab4226-2557-434a-9320-eefea6901a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cbfdb3ca-f1e1-4919-8484-857e8fd1efc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#获取数据\n",
    "data = pd.read_csv('./dataset/Income1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ec1c644-10c3-46a3-90ad-4b5b3d80546f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.658839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.401338</td>\n",
       "      <td>27.306435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10.842809</td>\n",
       "      <td>22.132410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.244147</td>\n",
       "      <td>21.169841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11.645485</td>\n",
       "      <td>15.192634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>12.086957</td>\n",
       "      <td>26.398951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>12.488294</td>\n",
       "      <td>17.435307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>12.889632</td>\n",
       "      <td>25.507885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>13.290970</td>\n",
       "      <td>36.884595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>13.732441</td>\n",
       "      <td>39.666109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>14.133779</td>\n",
       "      <td>34.396281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>14.535117</td>\n",
       "      <td>41.497994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>14.976589</td>\n",
       "      <td>44.981575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>15.377926</td>\n",
       "      <td>47.039595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15.779264</td>\n",
       "      <td>48.252578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16.220736</td>\n",
       "      <td>57.034251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>16.622074</td>\n",
       "      <td>51.490919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>17.023411</td>\n",
       "      <td>61.336621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>17.464883</td>\n",
       "      <td>57.581988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>17.866221</td>\n",
       "      <td>68.553714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>18.267559</td>\n",
       "      <td>64.310925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>18.709030</td>\n",
       "      <td>68.959009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>19.110368</td>\n",
       "      <td>74.614639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>19.511706</td>\n",
       "      <td>71.867195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>19.913043</td>\n",
       "      <td>76.098135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>20.354515</td>\n",
       "      <td>75.775218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>20.755853</td>\n",
       "      <td>72.486055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>21.157191</td>\n",
       "      <td>77.355021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>21.598662</td>\n",
       "      <td>72.118790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>80.260571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Education     Income\n",
       "0            1  10.000000  26.658839\n",
       "1            2  10.401338  27.306435\n",
       "2            3  10.842809  22.132410\n",
       "3            4  11.244147  21.169841\n",
       "4            5  11.645485  15.192634\n",
       "5            6  12.086957  26.398951\n",
       "6            7  12.488294  17.435307\n",
       "7            8  12.889632  25.507885\n",
       "8            9  13.290970  36.884595\n",
       "9           10  13.732441  39.666109\n",
       "10          11  14.133779  34.396281\n",
       "11          12  14.535117  41.497994\n",
       "12          13  14.976589  44.981575\n",
       "13          14  15.377926  47.039595\n",
       "14          15  15.779264  48.252578\n",
       "15          16  16.220736  57.034251\n",
       "16          17  16.622074  51.490919\n",
       "17          18  17.023411  61.336621\n",
       "18          19  17.464883  57.581988\n",
       "19          20  17.866221  68.553714\n",
       "20          21  18.267559  64.310925\n",
       "21          22  18.709030  68.959009\n",
       "22          23  19.110368  74.614639\n",
       "23          24  19.511706  71.867195\n",
       "24          25  19.913043  76.098135\n",
       "25          26  20.354515  75.775218\n",
       "26          27  20.755853  72.486055\n",
       "27          28  21.157191  77.355021\n",
       "28          29  21.598662  72.118790\n",
       "29          30  22.000000  80.260571"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "64793d10-59fc-489a-a601-a95272f55e45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '0     26.658839\\n1     27.306435\\n2     22.132410\\n3     21.169841\\n4     15.192634\\n5     26.398951\\n6     17.435307\\n7     25.507885\\n8     36.884595\\n9     39.666109\\n10    34.396281\\n11    41.497994\\n12    44.981575\\n13    47.039595\\n14    48.252578\\n15    57.034251\\n16    51.490919\\n17    61.336621\\n18    57.581988\\n19    68.553714\\n20    64.310925\\n21    68.959009\\n22    74.614639\\n23    71.867195\\n24    76.098135\\n25    75.775218\\n26    72.486055\\n27    77.355021\\n28    72.118790\\n29    80.260571\\nName: Income, dtype: float64')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NFJZyaOobIUHRXBBN3W1eyfbZ240bGBhjjDHGGGOsglR2DwIDqQSTu/gAQJlGhuLlyV18eIJH9kpwAwNjjDHGGGOMVZA3oQdBB18FlvVpBEdL1UYMR0s5p6hkrxTPwcAYY4wxxhhjFaS4B0FE9BlIAJXJHl9nD4IOvgq09XFEfPpD3HuSA/tqRY0a3HOBvUoS0jSTE2OMMcZYBcvOzoalpSWysrJgYWFR2dVhjLFXJi4pA1NjklUmfFRYyjG5iw/3IGBVjq7f39yDgTHGGGOMMcYqGPcgYG8jbmBgjDHG/kOICBIJ/3hljLE3gYFU8kpSUTL2puJJHhljjLH/EGNjY1y8eLGyq8EYY4yxtxD3YGCMMcaqoK+++krt84WFhZgzZw5sbYvumH333Xevs1qMMcYYe4txAwNjjDFWBX3//fdo0KABrKysVJ4nIly8eBFmZmY8VIIxxhhjrxU3MDDGGGNV0KxZs/DTTz9hwYIFaN26tfC8oaEhfv75Z/j4+FRi7RhjjDH2NuI5GBhjjLEqaNy4cdiyZQsiIiIwatQo5OfnV3aVGGOMMfaW4wYGxhhjrIpq0qQJTp8+jX///ReNGzdGUlISD4tgjDHGWKXhIRKMMcZYFWZubo61a9di8+bNCA0NRWFhYWVXiTHGGGNvKW5gYIwxxv4DevXqheDgYJw5cwY1a9as7Oowxhhj7C3EDQyMMcbYf4SLiwsUCgVkMv56Z4wxxtjrx3MwMMYYY1VQXFwczp8/DwBQKpWYPn06atSoAWNjYzg7O2POnDkgokquJWOMMcbeJnyLgzHGGKuCRowYgZUrVwIAIiMjsWjRIowfPx7e3t64dOkSZs+eDYlEgrFjx1ZyTRljjL2MQiUhPv0h7j3JgX01OZq628BAyhP5sjebhPj2BmOMMVblyOVyXL58GTVr1kS9evUwadIk9OjRQyjfsWMHRowYgdTU1EqsZVnZ2dmwtLREVlYWLCwsKrs6jDH2RopLysDUmGRkZOUIzyks5ZjcxQcdfBWVWDP2ttL1+5uHSDDGGGNVkI2NDe7cuQMA+Pfff+Hp6alSXqdOHdy+fbsyqsYYY0wPcUkZiIg+o9K4AACZWTmIiD6DuKSMSqoZY9pxAwNjjDFWBXXr1g0zZ85EYWEhwsLCsHTpUpU5FxYvXgw/P7/KqyBjjLFyK1QSpsYkQ10X8+LnpsYko1DJndDZm4kbGBhjjLEqaNasWcjMzISXlxdevHiB6OhouLu7o127dqhVqxbWrVuHhQsXVnY1GWOMlUN8+sMyPRdKIgAZWTmIT3/4+irFWDlwAwNjjDFWBVlaWuLo0aP4+uuv8eDBA7i5ucHY2Bh5eXn4+OOPkZSUhICAgMquJmOMsXK490Rz48LLvI6x142zSDDGGGNVlKGhIQYPHozBgwdXdlUYY4xVAPtq8gp9HWOvG/dgYIwxxqqw1q1b4/Hjx2Wez87ORuvWrV9/hRhjjL20pu42UFjKoSkZpQRF2SSautu8zmoxpjNuYGCMMcaqsIMHDyIvL6/M8zk5Ofjnn38qoUaMMcZeloFUgsldfACgTCND8fLkLj4wkGpqgmCscvEQCcYYY6wKOnfunPB3cnIyMjMzheXCwkLExcWhRo0alVE1xhhjeujgq8CyPo0wNSZZZcJHR0s5JnfxQQdfRSXWjjFx3MDAGGOMVUF+fn6QSCSQSCRqh0KYmJhg8eLFlVAzxhhj+urgq0BbH0fEpz/EvSc5sK9WNCyCey6wNx03MDDGGGNVUHp6OogItWrVQnx8POzs7IQyIyMj2Nvbw8DAoBJryBhjTB8GUgkCPWwruxqMlQs3MDDGGGNVkKurKwBAqVRWck0YY4wxxopwAwNjjDH2H5CcnIwbN26UmfDx/fffr6QaMcYYY+xtww0MjDHGWBWWlpaGbt264fz585BIJCAiAIBEUjROt7CwsDKrxxhjjLG3CKepZIwxxqqw4cOHw93dHffu3YOpqSkuXLiAv//+G40bN8bBgwcru3qMMcYYe4twDwbGGGOsCjt27Bj279+P6tWrQyqVQiqVIiQkBLNnz8awYcNw9uzZyq4iY4xVmkIlcSYGxl4jbmBgjDHGqrDCwkJUq1YNAFC9enXcuXMHdevWhaurKy5dulTJtWOMscoTl5SBqTHJyMjKEZ5TWMoxuYsPOvgqKrFmjP138RAJxhhjrArz9fVFYmIiACAgIABz587FkSNHMG3aNNSqVauSa8cYY5UjLikDEdFnVBoXACAzKwcR0WcQl5RRSTVj7L+NGxgYY4yxKmzChAlCqspp06YhPT0dzZs3x86dO/HDDz9Ucu0YY+z1K1QSpsYkg9SUFT83NSYZhUp1r2CM6YOHSDDGGGNVWPv27YW/PT09kZKSgocPH8La2lrIJMEYY2+T+PSHZXoulEQAMrJyEJ/+EIEetq+vYoy9BbiBgTHGGPuPuHXrFgDA2dm5kmvCGGOV594TzY0LL/M6xpjueIgEY4wxVoUplUpMmzYNlpaWcHV1haurK6ysrDB9+nRh6ARjjL1N7KvJK/R1jDHdcQ8GxhhjrAobP348Vq9ejTlz5iA4OBgAcPjwYUyZMgU5OTmYOXNmJdeQMcZer6buNlBYypGZlaN2HgYJAEfLopSVjLGKxT0YGGOMsSps7dq1WLVqFSIiIlC/fn3Ur18fQ4YMwcqVK/Hzzz+Xe323b99Gnz59YGtrCxMTE9SrVw+nTp0SyokIkyZNgkKhgImJCUJDQ5GamlqBe8QYY/oxkEowuYsPgKLGhJKKlyd38YGBlOepYayicQMDY4wxVoU9fPgQXl5eZZ738vLCw4cPy7WuR48eITg4GIaGhoiNjUVycjIWLFgAa2tr4TVz587FDz/8gOXLl+PEiRMwMzND+/btkZPDY5kZY2+ODr4KLOvTCI6WqsMgHC3lWNanETr4KiqpZoz9t0mIiPOzMMYYY1VUQEAAAgICyqSk/N///oeTJ0/i+PHjOq9r3LhxOHLkCP755x+15UQEJycnfP311xg1ahQAICsrCw4ODvj555/Rq1cvrdvIzs6GpaUlsrKyYGFhoXPdGGPsZRQqCfHpD3HvSQ7sqxUNi+CeC4yVn67f3zwHA2OMMVaFzZ07F507d8bevXsRGBgIADh27Bhu3ryJnTt3lmtdf/31F9q3b48ePXrg0KFDqFGjBoYMGYLPP/8cAJCeno7MzEyEhoYKMZaWlggICMCxY8fUNjDk5uYiNzdXWM7Ozn6Z3WSMsZdiIJVwKkrGXiMeIsEYY4xVYS1btsTly5fRrVs3PH78GI8fP8YHH3yAS5cuoXnz5uVaV1paGpYtW4batWtj165diIiIwLBhw7B27VoAQGZmJgDAwcFBJc7BwUEoK2327NmwtLQUHi4uLi+xl4wxxhirCniIBGOMMcYAAEZGRmjcuDGOHj0qPDds2DCcPHkSx44dw9GjRxEcHIw7d+5Aofi/8cs9e/aERCLBli1byqxTXQ8GFxcXHiLBGGOMVSE8RIIxxhj7jzp37pzOr61fv77Or1UoFPDx8VF5ztvbG7/99hsAwNHREQBw9+5dlQaGu3fvws/PT+06jY2NYWxsrHMdGGOMMVZ1cQMDY4wxVsX4+flBIpFAWydEiUSCwsJCndcbHByMS5cuqTx3+fJluLq6AgDc3d3h6OiIffv2CQ0K2dnZOHHiBCIiIsq3E4wxxhj7z+EGBsYYY6yKSU9PfyXrHTlyJIKCgjBr1iz07NkT8fHx+Omnn/DTTz8BKGqwGDFiBGbMmIHatWvD3d0dEydOhJOTE7p27fpK6sQYY4yxqoMbGBhjjLEqplu3bti3bx+sra0xbdo0jBo1Cqampnqvt0mTJvj999/xzTffYNq0aXB3d8f333+P3r17C68ZM2YMnj17hkGDBuHx48cICQlBXFwc5HK5yJoZY4wx9jbgSR4ZY4yxKsbExASpqalwdnaGgYEBMjIyYG9vX9nV0omuk0Qxxhhj7M3Bkzwyxhhj/1F+fn7o378/QkJCQESYP38+zM3N1b520qRJr7l2jDH231KoJMSnP8S9JzmwryZHU3cbGEgllV0txt5I3IOBMcYYq2IuXbqEyZMn4+rVqzhz5gx8fHwgk5W9ZyCRSHDmzJlKqKFm3IOBMVaVxCVlYGpMMjKycoTnFJZyTO7igw6+CpFIxv5bdP3+5gYGxhhjrAqTSqXIzMzkIRKMMVbB4pIyEBF9BqUvlor7Lizr04gbGdhbQ9fvb+lrrBNjb6yff/4ZWVlZlV2NKufGjRs4ceIETp48iQcPHrz27ZdOvxcfH4/jx48jNzf3pdZ38OBBvHjxoiKqprPCwkLcvXsX//7772vdLqt4ubm5L/3e04dSqawyjQuMsbdXoZJw7OoD/JlwG8euPkCh8s2+x1moJEyNSS7TuABAeG5qTPIbvx+MvW7cwMD+U65evYrWrVuXO27QoEG4c+dOuePu3LmDyZMno3fv3hg1ahRSUlLKvY5nz54hKioK48ePx48//qjThfr27dsxadIkHDlyBACwf/9+dOrUCR06dBDSyb2sR48eYd26daKvWbp0KVxdXeHu7o6goCA0a9YM9vb2CAkJwenTp/Xa/sWLF1GrVi3R11y/fh2NGzeGsbExOnbsiOzsbLRt2xbNmjVDUFAQfHx8cPny5XJvu127drh27Vq5417mHO7YsQMtWrSAmZkZnJyc4OjoCCsrK/Tt2xc3btzQGp+RkYFJkyahdevW8Pb2xjvvvIMuXbpg9erVZRpe1Ll3757KckJCAj799FMEBwfjww8/xMGDB7Wuo6SX+SwkJiZizZo1SEtLAwBcuHABQ4YMweDBg7Fr165ybf9lzoG29f39999aX7dnzx506tQJ1tbWMDU1hampKaytrdGpUyfs3btXrzrcvHkTn332mV7rYIyxN0FcUgZCIvfj45XHMXxzAj5eeRwhkfsRl5RR2VXTKD79ocqwiNIIQEZWDuLTH76+SjFWBVR6A0N+fn65Xl9QUIDExETs2rULu3btQmJiYrnXUVXdv3//laz3v3T8nj59ikOHDmkst7GxUfsoKChAYGCgsKyJqampcKc5OTkZPj4+2LhxI/Lz87Fjxw74+/vj3LlzonX08fHBw4dFX0Y3b96Er68vRo4ciT179mDy5Mnw8fERzXG/YsUKdOvWDTt37kSnTp0QHR2Nrl27okaNGnBzc8OIESOwaNEi0TqIuXHjBvr376+xfP78+Zg5cyZGjx6NFStWoG7dupgyZQp27NiBWrVqoUWLFjh16tRLbz8vLw/Xr18Xfc3XX38Nc3Nz/PHHH7CwsECnTp1QUFCAmzdv4vbt26hduzbGjh2rMb5Ro0ZqHwUFBejevbuwrIm+53D9+vX4+OOP0bRpU4waNQr29vYYM2YM5syZg5s3b8Lf3x+pqaka40+dOgVvb2/s3LkT+fn5SE1Nhb+/P8zMzDBq1Ci0aNECT548ET2GCoVCaGQ4evQomjZtiuvXryM4OFhosBG7wNb3s7Bt2zb4+/tjzJgxaNCgAfbu3YuQkBCkpqbi2rVr6Ny5MzZu3KgxXt9zoM2VK1fw7rvvir5m7dq16NSpEywtLbFw4UJs374d27dvx8KFC2FlZYVOnTph/fr1L12Hhw8fYu3atS8dzxhjb4LiYQalL9Yzs3IQEX3mjW1kuPdEc+PCy7yOsbdFuedg2L9/Pw4fPoyMjAxIpVLUqlUL77//PmrXri0a98svv6Br164wMjICAPz444+YN28ebt26BWtrawwbNkx0pmulUolJkyZhyZIlZbqyW1pa4ssvv8TUqVMhlb58m8ndu3exYsWKcs+43bp1a0RFRcHV1fWltw0U3TmOiYlBeHi42nIDAwO0atUKAwYMQPfu3WFsbFyu9et7DtQhIhw8eBBXrlyBQqFA+/btYWhoKBqTkZGBZcuWlXkfde3aFf369YOBgYHG2B9++EF03bdv38b8+fM13sGtVq0aWrZsiR49eqjsw8CBAzFt2jTUqFEDAPDpp5+qjS851rlr165QKpXYtm0bZDIZlEolevfujadPnyImJkZjHUuuo0+fPkhPT8fOnTthaWmJp0+folu3brCzs9N4cfXOO+9gxIgR+Pzzz3HgwAF06tQJCxYswJAhQwAUDfeYO3cukpOT1cZnZ2drrBsAnDt3Di1bttR4DN3d3bF06VJ07NgRAHD58mUEBQUhMzMTMpkMw4cPx8WLF7F792618V999ZXo9v/9919s3LhR9C68vb09du/eDT8/P2RlZcHa2hp///03QkJCAABnzpxBp06dkJmZqTbe0NAQoaGhaNasmfAcEWH69OkYPHiw0N188uTJauP1PYfe3t6YMmUKPvroIwBFDQbdunXDjRs3IJFI0KtXL+Tl5WHbtm1q40NCQtC2bVuhftHR0fjxxx9x/PhxPHr0CK1bt0aLFi1EG5pK7kO7du3g4uKC1atXC+UjRozA+fPnsW/fPq3xL/NZ8Pf3xwcffIDx48dj8+bNiIiIwFdffYWJEycCABYsWIDo6GicPXtW6/Zf5hxok5iYiEaNGom+D+vUqYPhw4dj6NChasuXLl2KhQsXamws+uuvv0TrkJaWhq+//lqnHilVCc/BwNjbo1BJCIncr7EngASAo6Uch8e2fuOyMhy7+gAfrzyu9XWbPm+GQA/b11AjxiqXzt/fpKO7d+9S06ZNSSqVkkwmI6lUSv7+/uTo6EgGBgY0evRo0XipVEp3794lIqI1a9aQXC6nSZMm0Y4dO2jGjBlkZmZGK1eu1Bg/evRosrOzo+XLl1N6ejo9f/6cnj9/Tunp6bRixQqyt7enMWPG6Lo7aiUkJJBUKtVY/ueff6p9GBgY0I8//igsv6rtSyQS6tChAxkZGZG1tTV9+eWXdPbsWZ3Xr+85ICLq2LEjPX78mIiIHjx4QAEBASSRSMjOzo6kUil5eXnRvXv3NMafPHmSLC0tyd/fn0JCQsjAwID69u1LH330EVlZWVFQUBBlZ2eLHgMnJydyc3NT+3BychI9hqmpqdSkSRMKDw+nJ0+eCM/LZDK6cOGC6L4Xb7/4GLq4uNDff/+tUn7mzBlSKBQ6r6NWrVq0e/dulfIjR46Qi4uLxngTExO6fv26sGxoaEjnz58XltPT08nU1FR0+1KpVOOjuFwTU1NTSk9PF5aVSiXJZDK6c+cOERW9j83NzTXGS6VSatSoEbVq1Urto3HjxqLbJyKqVq0apaWlERFRYWEhyWQySkhIEMpTU1OpWrVqGuMPHz5MHh4eNGnSJCosLBSef5n3wcuew5LHsHjbt2/fJiKiEydOkJWVlWj81atXheXCwkIyNDSkzMxMIiLavXs3OTk56bwPCoWCjh07plKelJRE1atX1yn+ZT4LZmZmwjFQKpVkaGhI586dE8qvXr0q+j7S9xxYW1uLPiwsLLS+D42NjSklJUVjeUpKCsnlctF9KP7MaXpoq0NVlJWVRQAoKyursqvCGHvFjl65T65jt2t9HL1yv7KrWkZBoZKazdpLbhrq7DZ2OzWbtZcKCpWVXVXGXgtdv7/L5rTSYNiwYXBycsKjR49gbGyMUaNGITs7G6dOncL+/fvRs2dP1KhRA8OHD9fUkCH8vXz5ckybNg2jR48GAHTq1Ak2NjZYunQpBg4cqDZ+3bp1WL9+Pdq3b6/yvJubGwYNGgRXV1eEh4cjMjJS4z5o67p+6dIl0fKuXbtCIpGo7Eux//3vfwCKUoJputuk7c6xti7NQFGXXKlUirVr12LNmjVYunQp/Pz8MHDgQPTu3Vu0NUnfcwAAcXFxwiRmEyZMwJMnT3D16lW4u7vj1q1b6Nq1KyZNmoRly5apjR8xYgRGjhwpeud1woQJGu+8urq6IjIyEj179lRbnpCQAH9/f4319/T0xNGjRzF+/Hj4+flh7dq1CA4O1vj60iQSCSSSohZ2qVQKS0tLlXIrKys8evRIp/UAQE5ODhQK1dmHa9SoITrhn62tLa5fv46aNWvizp07KCgowI0bN+Dr6wugaH4CsWEe1apVw/jx4xEQEKC2PDU1FV988YXG+Dp16mDPnj34/PPPAQAHDhyAkZERHB0dAQByuVzYP3U8PT0xcuRI9OnTR225tnMIFPXiWLNmDaZPn461a9fC1tYWmzdvRoMGDQAAmzZtQp06dTTGBwcH4/Tp0xg8eDCCgoKwYcMGeHh4iG6zNH3OoZubG06dOgU3NzcART0upFIpHBwcABQN5REbumRvb4+MjAxhroq7d++ioKBA+PzXrl1bGD4g5smTJ5DL5ZDL5WV6RMnlcjx//lxjrL6fhWrVquHBgwdwc3PD48ePUVBQoDJvwoMHD2Bubi5af33OQW5uLiIiIlCvXj215devX8fUqVNFt//OO+9g9erVmDt3rtryNWvWwMfHR2O8QqHA0qVLERYWprZcl88CY4y9yaryMAMDqQSTu/ggIvoMJIDKZI/Fv3Imd/F543peMFbpdG2xsLCwoKSkJGH56dOnZGhoKLRgrF+/nurWrasxXiKRCHe2q1evrnK3kYjoypUronccTU1NVe5ulZaYmEhmZmai+yB2t0iXO7cdOnSgzp07C3fNipXnrqc+d45L3rErdvToUfrss8+oWrVqZGpqSn379hWN1+cclK5D3bp1y/TY2Lt3L7m7u2uM1/fOa/fu3UV7qiQkJJBEIhHdh2L79u2jmjVr0jfffEOGhoY6n0MrKyuytrYmQ0NDWr9+vUr57t27yc3NTes66tWrRw0bNiRzc3P69ddfVcoPHTpENWrU0Bg/dOhQql27Ns2YMYOaNm1Kn376KXl5eVFsbCzFxcVRvXr16LPPPtMY36pVK4qMjNRYru0YbtmyhQwNDalnz54UHh5O5ubmNG7cOKF8+fLlFBgYqDH+k08+oREjRrz09omI4uLiSC6Xk5GREcnlcjp06BDVqVOHmjZtSs2aNSMDAwPasmWL6DqKrVmzhhwdHWnFihXleh/ocw5//PFHsrS0pDFjxtCkSZPIycmJBgwYIJRHR0dTw4YNNcYPHz6cfH19KTY2lvbv30/vvvsutWrVSiiPi4sjDw8PrftQ8n/PTz/9pFL+559/kqenp2i8Pp+FPn36UEBAAEVHR1OXLl2offv21KxZM7p48SKlpKRQy5Yt6cMPPxTdvj7nICgoiL7//nuN5dp6lBERHThwgMzMzKhevXo0cuRImjNnDs2ZM4dGjhxJ9evXJ3Nzczp06JDG+C5dutDEiRNF66Dr/7P+/fvTt99+q/LcN998Q/3799cp/nXiHgyMvT2qcg+GYrHn71CzWXtV6tts1l6KPX+nsqvG2GtV4T0YjI2NVe5KSqVSFBYWoqCgAAAQFBSkdfb1uLg4WFpaqr0zlpOTI3rXs1WrVhg1ahQ2bNiA6tWrq5Tdv38fY8eORatWrUS3b2Njg7lz56JNmzZqyy9cuIAuXbpojI+NjcXChQvRuHFjLF26FO+9957o9krT986xuuMTGBiIwMBA/PDDD9i8eTPWrFkjWgd9zkHpejx69KjMXV9PT0/RbAz63nmdNm2a6F3V8kzs1rp1a5w5cwaff/45zMzMROd+KBYVFaWy7OnpqbJ8/PhxdOvWTXQdpcf1l75LGxMTg+bNm2uMj4yMRF5eHjZv3oygoCAsXrwYP/zwA8LCwpCfn4+WLVti9uzZGuM/+eQT0VSMjo6OGuceAICePXuiWrVqiI6OxrNnz/Ddd98JvRkA4MMPP8SHH36oMX7BggWiqfwaNGgApVKpsRwA2rdvj4sXL+L06dPw9/eHm5sb/v77byxZsgTPnz/HrFmztE7QV6x///4ICQlB7969hf9n2uh7DocOHQqpVIro6Gjk5uaiX79+wtwDANC0aVPRuQNmzJiBjIwMdOnSBYWFhQgMDER0dLRQLpFIRN8DQFHPk5JK9wBIT0/HoEGDNMbr+1mYP38++vbti8GDByM4OBhbtmzBhAkT4OPjA4lEAg8PD5U5IUrT9xx07twZjx8/1lhuY2OjcT6cYq1atUJSUhKWLVuG48ePC3N+ODo6omPHjhg8eLDQS0Wd0aNH49mzZxrLPT09y5wnTdLT08t8bm7fvo2bN2/qFM8YY69CU3cbKCzlyMzKUZvusXgOhqbumnteVrYOvgq09XFEfPpD3HuSA/tqRfXlnguMqafzJI8ffPCB0DXfyMgIY8aMwfbt24XJq06cOIGuXbsiI0P9TLClJ1+cPn06xo8fLyyvXr0aS5YswZkzZ9TG37x5E506dUJKSgrq1asndCW+e/cuzp8/Dx8fH2zfvh0uLi4a96F9+/Zo3rw5JkyYoLY8MTERDRs21Hpxk5CQgN69eyMkJAQLFy6EpaUlEhMTRbvCAsC7776Ljh07YsyYMS+1/ZKTmr0Mfc9B8To6duwIY2NjHDx4EOvWrVNpaDlx4gTCwsI0Tq43YsQI7Nu3D/PmzYOxsTGmT58OIhJ+RO/atQtDhw7FlStXXmof32Y5OTnIz89HtWrVKrsqVZJSqcSTJ09gYWGhU0PbmyAnJwcFBQVahxJUJWlpaXj+/Dm8vLwgk+ncBs6qEJ7kkbG3S3EWCUD9MINlfRqhg6+iTBxj7M2i6/e3zr/e5s+fj3bt2sHKygoSiQRmZmbYunWrUH7x4kX069dPY7y2i3YHBwfRO24uLi5CesqSd4qaNm2KWbNmoV27dlozSAwePFj0blHNmjXL3JVTx8/PD6dOncLIkSPh5+endk4GdfS9cxwVFVVmnHN56HsOANXsCmFhYWV6E/z222/w8/PTGK/uzmvJNG663HlV5+7duyAiYR4AbZ49e4bTp08LWSw8PDzQsGHDKnNhqU7xWPrKVlBQgDt37qBmzZqvdDuFhYW4fv063NzcIJVKkZubiz///BNKpRLvvvuu0AipK3XzCLxqBQUFuHDhgsqdbx8fH62ZWIpV1Pm+ceOGSkYXW9uXnw2biKBUKnXqEaROce+mqqYijyFjjP2XdPBVYFmfRpgak6ySTcLRUo7JXXy4cYGx/5ryjLt49uwZ7dq1i2JiYujff/99ydEb/y1//vknjRgxoszcCG+rp0+f0osXL7S+7sWLFypZHHT14MED6t69O7m4uNDgwYOpoKCABgwYIIwnDwwMFLIZqFNYWEijR48mU1NTlfHnEomEXF1d6a+//hLdvrm5OX322Wd05MiRcte9pISEBOrbty+5u7uTXC4nU1NT8vX1pQkTJmgd1/Tee+/RunXr6Pnz53rVodjTp09pzZo19O2339LixYvp/n39xkHqMnZ9yZIl1KZNG+rRowft3btXpezff/8VnceDqGjOFYVCQVKplHx9fenGjRvk6+tLZmZmZG5uTtbW1hQfH/9K9yEhIYGmT59OS5YsKfP/MCsrS3Tse2FhIY0fP56srKzKzAdjZWVFEyZMUMluoc7KlSspPDyc1qxZQ0REmzdvJi8vL3J3d6dJkybptJ9LliyhmjVrlpkPJjg4mE6dOiUam5+fT+PHj6cWLVoI25s7dy6ZmpqSkZERhYeHU25urk71UCczM5OmTp2qsbz0/9yzZ89SeHg4BQUFUffu3enAgQM6befmzZtq/xfl5eWJzp9QTJ9j6OvrS9OmTaMbN27oVFcx69ato6CgIFIoFHTt2jUiIvruu+/ojz/+0HvdFY3nYGDs7VRQqKSjV+7TH2dv0dEr9zn7AmNVjK7f3+VqYNCXUqmktLQ0ys/PJyKi3Nxc2rx5M61du1bvBounT5/q9GNQnZJ1qgpOnDhB33//PY0bN47GjRtH33//PZ04cUKn2Lt379K+ffuEVJOZmZkUGRlJs2fPVkl1+Lrk5ORQTk6Ozq//7LPPyNfXlxYvXkwtW7aksLAwql+/Ph0+fJiOHj0qpKDUZOzYseTt7U0xMTG0Z88eatGiBUVGRtLFixdp4sSJZGxsTLt27dIYL5FI6J133iGJREJeXl40f/580bSc6sTFxZGJiQl1796d+vTpQ6ampvTll1/S2LFjydPTkzw8PCgjI0O0DjKZjCwtLWnw4MFaL2JK8/b2pgcPHhAR0Y0bN8jNzY0sLS2pSZMmZGNjQ/b29kIKyJeh7eJ80aJFZGpqSkOHDqU+ffqQkZERzZo1SyjPzMzUenHfvn17+vDDD+n8+fM0fPhw8vb2ph49elBeXh7l5+dTnz59KDQ0VK99EJtcb9euXWRkZETvvPMO1axZk2xtbWn//v0674O+aXcXLlxIZmZm9MEHH5BCoaAZM2aQra0tzZgxg6ZOnUoWFha0YsUK0X2cN28eOTk50eLFi2nlypXk7e1N06ZNo9jYWOrbty+ZmprSyZMnNcZPmDCBHBwc6KuvviIfHx8aPHgwubi4UHR0NK1du5Zq1KghOpmoNtreRyXT7h45coQMDQ2pZcuWNHr0aGrbti3JZDLR74Q7d+5QkyZNSCqVCulySzY06PI+1PcYSiQSsrW1JQMDA2rfvj39+uuvL/VdtHTpUqpevTrNmDFDZSLdqKgolck/3xTcwMAYY4xVPa+kgSE5OZnWrFlDFy9eJCKiixcv0uDBg6l///60b98+0diUlBRydXUlqVRKnp6elJaWRv7+/mRmZkampqZUvXp1unz5cnmqo0KXO46aGBoaUnJysk6v1ecYlFbeO8d3796lkJAQ4W5706ZNqWnTpuTq6koSiYRCQkJEe1IUz3gukUjI0dGREhISyNnZmWrXrk1169bVenFd7M6dOzRx4kR69913ycvLi3x8fOi9996jVatWUUFBgdb43bt3U8eOHcnKykq422dlZUUdO3akPXv2iMYqFAqh90BmZiZJJBLavXu3UH748GHRmeMVCgX9/fffwvKtW7fI3NxcaOSYNm2aaAaE4iwaCQkJ9OWXX5KNjQ0ZGRnRBx98QDt37iSlUntrvJ+fHy1btkxY3r17N3l5eRFR0V3TNm3aUL9+/UTrcOHCBVq4cCHVq1ePpFIpNWjQgBYvXkwPHz7Uuv2SmUB69+5NQUFBQoPTkydPKDQ0lD7++GON8Q0bNhR9eHl5iX4WfXx8aMOGDcLykSNHyM7OTphNX5cLO2tra+Ez+/z5czIwMFBpZEtKSiJbW1uN8d26dRN9tG7dWrQOgYGBwoz9SqWSIiMjydzcnGJjY3XaBwcHB4qLi9NYHhcXR/b29hrLvby8hGN45swZkslktGrVKqF81apV5O/vrzGeiMjNzY127twpLF+6dIlsbW2FC9xhw4ZR27ZtNcbXqlWLYmJiiIgoNTWVpFIpbd68WSjfsmUL+fr6aoxPTEwUfWzZskXnrDpt27Ytkzll+PDh1Lp1a43x4eHhFBAQQCdPnqQ9e/aQv78/NW7cWPgMFf9/EaPvMZRIJHT79m36/fffqUuXLiSTycjOzo6+/vprnb+TiIoaDX///XciKuplVdzAcP78edHPQWXhBgbGGGOs6qnwBobY2FgyMjIiGxsbksvlFBsbS3Z2dhQaGkqtW7cmAwMD0QvssLAwev/99+ncuXM0YsQI8vb2prCwMMrLy6OcnBzq0qUL9enTR/c9LEWXBgZNFxNSqZRCQ0OF5Vd1DPS9c9y9e3cKDAyklJSUMmUpKSkUFBQkmtYtJCSEhg4dSk+ePKF58+ZRjRo1aOjQoUL5qFGjKCgoSGM8EdHJkyfJ0tKS/P39KSQkRLjz99FHH5GVlRUFBQVRdna2xviff/6ZZDIZ9erVi6Kiomjnzp20c+dOioqKoo8//pgMDQ1p3bp1GuNNTU2F7r9ERY1DJXtepKWliaYrrVatWpk0mTKZTOgxcOHCBTI1NdUYXzpVaE5ODm3cuJHatGlDUqmUnJ2dRdPOERHJ5XJKT08XlpVKJRkaGgpDO/7++2+ys7PTuQ4nTpygQYMGkaWlJZmYmNDHH38s+j4sGV+rVi2VBhqiogt+FxcXjfHGxsb06aef0pQpU9Q+vvjiC9HPoomJicr+ExVdCDk4ONC4ceN0amCwsrISGiTz8vLIwMCATp8+LZRfvHiRrK2tNcbLZDLq2LEj9evXT+3j/fffF62DhYUFXblyReW5DRs2kJmZGcXExGjdB33T7pqYmND169eFZWNjY5U0wqmpqWRlZaUxvrgOpd+HMplMeB8mJCSQubm5xni5XK7StV8ulwsNr0RFn0WxtLf6pg0u+T5WKBR07NgxlfKkpCSqXr26xngnJyeVRqni7yE/Pz968OCBTu9DfY9h6c/ynTt3aNasWVS7dm1hyNfq1atF60BUdOyL/y+WbGC4fPkyyeVyrfGvGzcwMMYYY1VPhTcwBAYG0vjx44mIaNOmTWRtba2Sc3vcuHGid2rs7Ozo7NmzRFR0514ikdA///wjlB85coRq1qypMd7a2lr0YWFhofXHoEQioZYtW5a5mJBKpdS1a1dh+VUdA33vHJubm9OZM2c0lp86dUr0x2zJi6L8/HySyWTCOSEq+jFqaWmpMZ6IKDg4mKZMmSIsr1+/ngICAoiI6OHDh+Tn50fDhg3TGF+7dm368ccfNZYvWbKEPD09NZY3aNBAiN+5cydVq1aNFixYIJQvW7ZM9K5pUFAQzZgxQ1jetGmTyoXY+fPnRS9MS3bLLi09PZ0mTJggenFOROTh4aFy9zo1NZUMDAyE8eppaWlkYmKiMb70RUmxZ8+eUVRUFIWEhGi9MCse1uHk5FRmaMy1a9dEL0r8/f1p6dKlGsvPnj0run0XFxeVXiTFLly4QA4ODhQeHq71s9ymTRsaMGAA3bp1i6ZOnUqenp4qcx4MGTKEmjdvrjG+Xr16Knf8y7sPdnZ2aoembNq0iUxNTWnZsmWi8Z06daJ27dqpHRr277//UocOHahz584a421tbVXucDs7O6s0vKWmpor+LyAq6knz008/Ccv79u0jU1NToRdOSkqKaAOBg4ODSiNJUFAQ3bp1S1i+ePEiWVhYiO7D6tWr6dq1a2ofO3bs0Po+vnLlCmVlZZG7u3uZ/41XrlwRbSw0MzMr02suPz+funbtSvXr16dz585pfR/qewzF/p8cOHCA+vTpI9rQVMzb21uYa6FkA8MPP/xADRs21Br/unEDA2OMMVb1VHgDg4WFBaWmphLR/931LfmDrvgOpCal77iZm5ur3AG8ceMGGRsba4w3NTWlr7/+mn7++We1j6lTp2r9Mbhp0yZydnYWJkUrJpPJ6MKFC6KxRPofA33vHNva2tLBgwc1lh84cEC0O2z16tWFu5zPnj0jqVSqctcvMTFR9I4fEamM7yUqOg6GhoaUmZlJREXd/Z2cnDTGGxsbq+2BUSwlJUX04jY6OpoMDAzI09OTjI2NaevWreTk5EQ9e/akXr16kZGRkWgDxt69e8nY2JiaNm1KLVq0IJlMRgsXLhTK582bJ9qtWtPFfUnahklMnTqVnJ2dadmyZbRmzRry9fVV6Tmzbds28vHx0asOly5dEo2vV68eNWzYkMzNzenXX39VKT906JDoMJNhw4bR8OHDNZZfuXJFdNz3xx9/TCNGjFBblpSURHZ2dlo/y/Hx8WRra0tSqZTs7OwoKSmJAgICyNHRkZycnMjExKTM5JEl9evXj4YMGaKxPDk5mdzc3DSWt23blubNm6e2bOPGjWRoaCi6D8WTUspkMmrYsCF16NCBOnToQA0bNiSZTEb169cXnfgvODhYZThCaTExMaINbURFQxgMDQ2pZ8+eFB4eTubm5jRu3DihfPny5aLDhd599136+eefNZb/8ssvosM02rVrR9OnT9dYrm0ejOIeDsW9HUpe6BMVTcAr1lhZr169Mu99ov9rZCieuFGMvsdQl8+yLhfhK1eupBo1atDmzZvJzMyMNm3aRDNmzBD+ftNwAwNjjDFW9bySBoaSDQIl75IQab/r6eHhodJjYenSpSpd6U+fPk2Ojo4a44OCguj777/XWK7rHAzp6ekUHBxMH3zwgTDWtjwNDPocA33vHA8ZMoRcXV1p27ZtKic2KyuLtm3bRm5ubvTll19qjA8LC6P33nuPDh8+TIMGDaLGjRtT586d6enTp/Ts2TP68MMPqUOHDpoPABG5urrS4cOHheU7d+6QRCIRMhqkp6eL7kOjRo1o9OjRGsvHjBlDjRo1Eq3D4cOHaf78+cJcDBcuXKC+fftS9+7dRS94iiUkJNC3335LX3/9dZlGHm2mTJlCz549K1dMafn5+TRmzBhycnIiW1tb+uSTT1TuZJ84cUJ0crpWrVrRo0ePXnr7pYc0lJ4LYNSoUdSrV6+XXr82iYmJZRr5Sjp//rxKLxlNnj59SqdOnRIm5nvx4gWtWrWKFi9eLNqIRVTUHV6f87ht2zaNjSRERcMltE2uV1hYSDt37qRJkybRoEGDaNCgQTRp0iSKjY3VmkHi8OHDKr2PSluyZAktXrxYdB1ERb2APvnkE+revXuZC/T79++Lzgtz6dIl0SFdGzZsoC1btmgs37ZtG61fv15j+cOHD0U/zwcPHlR5lG5U+/7772nu3Lka48eMGUPt2rVTW5afn691mEwxfY5hv379RIeUlUd0dDR5enoKw0xq1Kgh2kunMnEDA2OMMVb1VHgDQ/369YUJzIiKLgJKznb9999/i6aW++KLL2jlypUay2fPnk2dOnXSWD5z5kzRi44bN26IDm8oqbCwkCZNmkQuLi4UFxdHhoaGOjUw6HsM9L1znJOTQ4MHDyYjIyOSSqUkl8tJLpeTVColIyMjioiIEM3IcPnyZapduzZJJBLy9vamW7du0fvvv08ymUyYXKzkOHZ1hg8fTr6+vhQbG0v79++nd999V+VCKi4ujjw8PDTGF080Wa9ePRo5ciTNmTOH5syZQyNHjqT69euTubn5S2cDYYwxXeXn54t+Qebn56sMO6kqnj179sanTeYGBsYYY6zq0fX7W0JEBB0sX74cLi4u6Ny5s9ryb7/9Fvfu3cOqVat0WV0Z6enpkMvlUCgULxX/Mg4fPozw8HBcv34d58+fh4+Pj+jr9T0GU6dOVVlu1qwZ2rdvLyyPHj0at27dwqZNm0TrkZ2djdOnTyMzMxMA4OjoCH9/f1hYWIjGFXvw4AFsbW2F5X379uHFixcIDAxUeV6dp0+fYsCAAdi2bRsKCwsRGBiI6OhouLu7AwB2796NrKws9OjRQ+M6rl27hmXLluH48eMq+xAYGIjBgwfDzc1Np/3QR1paGg4fPoyMjAxIpVLUqlULbdu21fkYlpSfn49r167B3t4elpaWr6C2b6aKOoaPHz/G1q1bcePGDbi6uqJHjx46H8fSdfDw8EBoaKjWOpw+fRr+/v7lqqcmN27cUDkG2j5DJcXHx+PYsWMqn4OgoCA0adKk3PVITU0VjqGnp2e540srKCjAnTt3ULNmzUqJL892Lly4oHIMfXx8YGho+Eq3C1Ts+6ik/Pz8l6r/vXv3cOnSJQCAl5cX7OzsKrpqFSI7OxuWlpbIysp6qf+5jDHGGHv9dP7+fi3NHSJ0Sev3Kj158oQSEhJE7/yzsl68eKGSM/51WrJkCbVp04Z69OhRZpz9v//+K9qL5OnTp/Thhx+qzFbv6OhIBgYGZG5uLjp/AxFRZGSkMBykoKCAvv76a6FHiUwmo/79+1NeXp7oOk6cOKGSzjMmJoZatGhBTk5O5O/vT2vXrn2l8URFw0SmT59OS5YsKTPRYFZWlsqEiaXpewy7detGW7duJaL/m+nfzs6OAgICyMHBgRwdHbWm6NO3DhKJhDw8PGjmzJl0+/Zt0ddqsmTJEmGcfslHcHCw2gkgS9I35eysWbOE9/7Dhw+pTZs2KseiQ4cOeg2jIdIv9a+u8Xfu3KH169fTjh07hElOiz19+pSmTp2qMbawsJDGjx9PVlZWZbJQWFlZ0YQJE7QONblw4QJFRESQn58fOTo6kqOjI/n5+VFERIROvdr0fR9t2bJFZb8XL14svKdsbW1F97+k7Oxs6tOnDxkYGAjHQCaTUe/evYWJhN8k3IOBMcYYq3oqfIiEGH0aCQwNDcuV77s0d3f3MjOBl1dmZqaQpvBllBwm8aolJyfTmjVrhHRwFy9epMGDB1P//v1FUxMWS0hIoNWrVwtzRyQlJVFERAR98cUXZcbiv4kWLVpEpqamNHToUOrTpw8ZGRnRrFmzhHJtqeUGDRpEwcHBdP78eUpNTaUPP/yQxowZQ8+ePaPVq1eTqakpbdiwQWN8yVnf582bR9bW1rRmzRq6cOECRUdHk729PUVGRoruQ8l1/PXXXySVSik8PJyWLFlCAwcOJJlMRtu2bXtl8bt27SIjIyN65513qGbNmmRra0v79+8Xyl/1MbS2thbevx07dqRPPvlEuMjKy8ujAQMGaBwbX1F1kEgk9Pnnn5O9vT3JZDLq3Lkz/f777yoNN2LmzZtHTk5OtHjxYlq5ciV5e3vTtGnTKDY2lvr27UumpqZ08uRJjfH6ppx1dnYWJpgdOHAgNWzYkM6cOUMvXryghIQEatasGQ0YMECnfdHkVTcwxMfHk5WVFVlYWJCJiQl5enqqpNrU9j4cPXo02dnZ0fLlyyk9PZ2eP39Oz58/p/T0dFqxYgXZ29vTmDFjNMbv3LmTjIyMqFmzZjR58mRaunQpLV26lCZPnkxBQUFkbGys9X+ivu+jkp/lNWvWkFwup0mTJtGOHTuESRrFhhYW69mzJ9WuXZvi4uIoKyuLsrKyKC4ujurWrUsfffSRTnV5nbiBgTHGGKt6KryBIScnh77++mtq3rw5zZkzh4iIpk+fTmZmZmRmZkYff/yx6MZGjhyp9lF8cVS8rMmiRYvUPgwMDOibb74RlsU8ePCAunfvTi4uLjR48GAqKCigAQMGCHf9AgMDhfzl6sTGxgpp2QoLC2natGnk5OREUqmUatSoQbNnz9ba2JKQkEB9+/Yld3d3ksvlZGpqSr6+vjRhwgStJys2NpaMjIzIxsaG5HI5xcbGkp2dHYWGhlLr1q3JwMBAtJHht99+IwMDA7K1tSVzc3Pas2cPWVlZUWhoKLVv354MDAxEL8qIqMxd1bNnz1J4eDgFBQVR9+7d6cCBA6Lx2mi7KPHx8VGp45EjR8jOzo4mTpxIRNovSqpXr65yd/nhw4ckl8uFCf9+/PFH8vPz0xhfctb3hg0b0ooVK1TKo6Oj6Z133hHZQ9V1hISEqMw6T1Q030izZs1eWXxgYKCQXlWpVFJkZCSZm5sL84u86mNoYmIiTJaqUCjKpBe8dOmS1nSpFXUe8/Pz6ddff6VOnTqRgYEBOTg40JgxY0SzcBARubm50c6dO1XqbGtrKzQ2Dhs2TDRlrb4pZ42NjYX5Adzc3MrMW3Lq1ClSKBSi+9CwYUPRh5eXl+j7QN/40NBQ6t+/PxUWFlJ2djZFRESQra2tcFy0vQ8dHBxEGwDi4uLI3t5eY3n9+vWF/xvqTJ48merVq6exnEj/91HJz3LTpk3LTEq5dOlSndJMmpqaqkyiXOzvv/8WTdVZWbiBgTHGGKt6KryBYeTIkeTk5ERff/01eXt705AhQ6hmzZoUHR1NGzduJE9PT/rf//6nMV4ikZCfnx+1atVK5SGRSKhJkybUqlUrevfdd0XjnZ2dyc3NTeVRPFu2m5ubaNd4IqLPPvuMfH19afHixdSyZUsKCwuj+vXr0+HDh+no0aPUpEkTCg8P1xhft25d+vvvv4moqIuyra0tfffddxQbG0vff/89OTg4CI0v6sTFxZGJiQl1796d+vTpQ6ampvTll1/S2LFjydPTkzw8PER7UgQGBtL48eOJqCjlprW1tXChSEQ0btw40YuaRo0a0YwZM4R4KysrmjZtmlA+f/580YsyItU7bkeOHCFDQ0Nq2bIljR49mtq2bUsymUyvSRq1paYzMTGh9PR0leeK04OOGzdO60WJlZWVSo+XvLw8kslkQnaPy5cv65wJxNbWtkwmkLS0NK0/6EteVNjb25fpTp+SkkJWVlavLL50NhSiohn/zczMKCYm5pUfw4CAAGG2/YYNG9Lvv/+uUr57927RjDIVUQd16QFv3bpF06ZNo1q1apFUKqXmzZtrjDc1NVV5HyqVSpLJZEIDZUJCgmgDgb4pZ+vUqUPbt28noqJeXMUZVYqdPXuWLCwsNMYTFTVSfPrpp2WyihQ/vvjiC9H3gb7x1tbWZS7AZ8+eTdbW1hQfH6/1fWhqaio0+KqTmJhIZmZmGsvlcrleKXOJ9H8flfx/Ur16dUpISFApv3LlClWrVk20DkRELi4uao9FYmKi6MTBlYUbGBhjjLGqp8IbGFxcXGjPnj1ERHT16lWSSqX0xx9/COW7d+8mV1dXjfGzZ88md3f3MnfYdU0R+cUXX5Cfn1+Z4RS6xhMV3S0t/iGemZlJEolEJU3h4cOHRX+MGRsb0/Xr14mIyNfXl3755ReV8u3bt4vmXffz86Nly5YJy7t37yYvLy8iKrpAatOmjWgmDAsLC0pNTSWioh4UMplM5S5o8YW2JmZmZsJFkVKpJENDQ5UfpVevXhW9KCJS/UHdtm1b+uyzz1TKhw8fTq1bt9YY361bN9FH69atRS8qXFxchEaeki5cuEAODg4UHh4uGt+2bVsaOnSosDxv3jyVO71nzpyh6tWra4yXSCQ0c+ZMWrRoESkUijKNKYmJiWRtba0xvngdBw4coMTERHJ1daX4+HiV8pSUFNHzoG+8nZ2d2jkCNm3aRKamprRs2bJXegy3b99ONjY2FBUVRVFRUeTm5karVq2iI0eO0Jo1a8jFxUU0lWlF1KFkQ5k6e/fupU8++URjuZ+fn0pKwn379pGpqanQgyklJUX0wlDflLPz5s0jb29vSk1NpQULFlBgYKDQaJSWlkatWrUSHWJBROTv709Lly7VWH727FnR94G+8dbW1pSYmFjm+Xnz5pGVlRVt27ZNNL5Tp07Url27MnOIEBXNxdKhQwfq3LmzxngvLy9asGCBxvIFCxZQ3bp1NZYT6f8+kkgktG7dOvrzzz/J2dmZjh49qlKelJSktaGIiGjFihUUGhqq0kCdkZFB7dq1o+XLl2uNf924gYExxhireiq8gcHExES4uCYqmjuh5HjZ9PR0rXdu4+PjqU6dOvT1118LE+GVp4Fg27Zt5OLiopLfvTzxpqamKmnHDA0NVe5Ap6Wlid7xUigUdOzYMSIq6p5buovz5cuXycTERGO8XC4vc9fT0NBQuOv5999/k52dncb40neezc3NhbkUiIiuXbsmesfN0dFRuLB8+PChcKFaLD4+Xuud45INDCWPR7HiSfs0kclk1LFjR+rXr5/ah7bc8x9//DGNGDFCbVlSUhLZ2dmJxp8+fZpsbGzI0dGRatasSUZGRrRp0yah/McffxTtxeLq6qrSg2bhwoUq5d9//73o8AQiEobkFE/GVnodmzZtIh8fn1cW37ZtW5o3b57aso0bN5KhoeErPYZERL/++is5Ozur7IdEIiG5XE4jRozQOoZd3zqou/NcHlu2bCFDQ0Pq2bMnhYeHk7m5ucpQleXLl1NgYKDGeH1TzhIR/e9//yNDQ0Py8vJSiZVKpdS4cWOt88oMGzaMhg8frrH8ypUrKiloKzq+efPmKg2uJUVGRpKxsbHo+/DGjRvk6+tLMpmMGjZsSB06dKAOHTpQw4YNSSaTUf369enGjRsa43/55ReSyWTUpUsXWrRoEW3evJk2b95MixYtovfff5+MjIzKpBIuTd/3UenJKYt7mBVbtWqVTkMk/Pz8yNzcnAwNDcnDw4M8PDzI0NCQzM3NywxdeRNwAwNjjDFW9VR4mkovLy9MnToVH330EU6ePImQkBAsX74c/fv3BwBs2bIFEydOxOXLl0XX8/TpUwwdOhQJCQnYsGEDGjVqhISEBK0pIovdvn0b4eHhMDIyQlRUFFxcXJCYmKhTvJ+fHz7//HMMHToUsbGx+OijjzBlyhR89dVXAIrSUC5ZsgTnz59XGz906FDcuHEDf/zxB4YMGQKlUomffvoJEokEADBs2DCcOnUKR48eVRvv6emJJUuWCKkpr1y5Ai8vLzx//hxGRkZIT0/HO++8g+fPn6uNb9CgASIjI9GhQwcAQFJSEry8vCCTyQAA//zzDz799FOkpaWpje/bty9SU1Pxv//9D1u2bEFeXh6ysrIQFRUFiUSCL774AnZ2dti6davGYyiVSpGamgo7Ozv4+fnht99+Q8OGDYXyq1evon79+nj27Jna+Pr162P48OEYMGCA2vKEhAT4+/ujsLBQbfm5c+dw+vRp4X1XWlJSEn777TdMnjxZ4z5kZGRg+/btyM3NRevWrXV+7+ni+PHjMDY2VjkmpV2/fl1l2dzcXCW14bp16wAA4eHhryT+999/x99//42FCxeqLd+4cSNWrlyJAwcOaNyHijiGhYWFOHPmDNLS0qBUKqFQKODv749q1arpFK9PHQ4dOoTg4GDhs/MyYmNjER0djdzcXLRv3x6ff/65UPbgwQMA0JqyMjs7G6dOncLdu3cBlD/l7MWLF7F9+3aVYxgcHIzQ0FDh/9KbatWqVTh06BDWr1+vtjwyMhLLly9Henq6xnUolUrs2rVLbcrbdu3aQSqVitbh6NGj+OGHH8qkCg0MDMTw4cMRGBgoGl8R7yMx27dvh6GhoUo6Y3VKp0AWI/a/8XXhNJWMMcZY1VPhaSoXLlxIcrmcQkNDydramn744QdydHSkMWPG0Lhx48jS0lJlPL82mzZtIgcHB5JKpTr3QCimVCpp1qxZQlo6XeOjo6PJwMCAPD09ydjYmLZu3UpOTk7Us2dP6tWrFxkZGYmmt3v8+DE1btyYPD09qW/fviSXy8nV1ZXatm1L7u7uZGlpScePH9cYP3XqVHJ2dqZly5bRmjVryNfXl7p16yaUb9u2TfTO87Jly4Rx1+p88803ojPHZ2ZmUtu2bcnc3Jzat29Pjx8/pi+//FK4I167du0yY/NLK35t8Z3nkt3EiYj+/PNP0WEi/fr1oyFDhmgsT05OJjc3N9E6MMYYq7q4BwNjjDFW9VR4Dwag6M7msWPHEBQUhI8//hgHDx7EpEmT8Pz5c3Tp0gUTJ07UeseopJs3b+LMmTNo06YNzM3NdY4rdvr0aRw+fBjh4eGwtrbWKebIkSM4fvw4AgMDERQUhOTkZMyZM0fYh08//VQ0Pj8/H6tXr0ZMTEyZu4YRERFwdnbWGFtQUIDx48er3PVctGgRqlevDgCIj49HTk4OWrRooftBqABpaWl4/vy5Sm8ITQ4dOqSyrFAoUKdOHWF50aJFyMvLw+jRo9XG5+bmorCwEKampvpXHMDjx4+xdetW3LhxA66urujRowcsLS01vn7BggX48MMP4erqWiHbL+3Ro0eIiYnR2HugpGfPnuH06dPIyMiAVCpFrVq10KhRo0q78zx16lQMHTpUeD+KycvLwx9//FHmzm9QUBDCwsJgZGSkMTY3NxdSqRSGhoYAinq9rFmzRjiHAwYMgLu7u177ost5ePDgAc6dO4cGDRrAxsYG9+/fx+rVq5Gbm4sePXrA29tb63YyMzNx4sQJlWMQEBAAR0dHrbEvXrzApk2bcPjwYZX3QNeuXdGmTRvdd7aE/v37Y+bMmXByctL62t9++w0dO3bU67N47949JCUlwd/fH5aWlrh79y7Wrl0LpVKJzp07o169ei+97vIq7/+CipKYmIjTp0+jVatWqFWrFi5cuIAlS5ZAqVSiW7duWnsfAEX/+9X1omjatKlOdfj0008xYMCA1/7d8bK4BwNjjDFW9VR4D4ZXpTitG2O66NatG23dupWI/m++Bzs7OwoICCAHBwdydHQsMxFoSRKJhAwMDCg0NJQ2b95Mubm5FVo/bWk2iYgKCgpo9OjRZGJiotIbRCKRkKurK/3111+i8Xl5eTR69Gjy8PCgJk2a0OrVq1XKtc2+n5WVVebx+PFjMjQ0pBMnTgjPaZKamkq1atUiuVxOLVu2pJ49e1LPnj2pZcuWJJfLydPTU5iMVJ2WLVsK5/Dw4cNkbGxM9evXp48++ogaNmxIpqamZSa7Ky9t5+HEiRNkaWlJEomErK2t6dSpU+Tu7k61a9cmDw8PMjExodOnT2uMf/r0KfXu3ZukUinJZDKyt7cne3t7kslkZGBgQH369BFSZqqTmppKrq6uZG9vTy4uLiSRSKhz584UEBBABgYG1KNHD9H/jYmJiWofhoaG9PvvvwvLYiQSCVlYWNDnn38u2vNKkwMHDpCZmRlJJBJydHSkhIQEcnZ2ptq1a1PdunXJ2NiYdu3apTH+xIkTKnNtxMTEUIsWLcjJyYn8/f1p7dq1otvX93+Bvtsn0j/17927dykkJET47Ddt2pSaNm1Krq6uJJFIKCQkRKc5HsLCwsjQ0JA8PT1p5syZdOvWLa0xlYl7MDDGyqugUElHr9ynP87eoqNX7lNBoXhaeMZYxavwSR41yczM1DqZGBFRbGyskLGgsLCQpk2bRk5OTiSVSqlGjRo0e/ZsYQZ2Te7cuUPr16+nHTt2lLkwfPr0KU2dOlXnel+/fp2OHz9O8fHxdP/+fZ3jXgVdj2HpH5pnz56l8PBwCgoKou7du6tM2KjOl19+qTYDQ0WYMmWK2tnctcnPz6fdu3fTqlWraM+ePVon97O2tqaLFy8SEVHHjh3pk08+Ed4LeXl5NGDAAGrXrp3GeIlEQlFRUcIPcltbWxo+fHiZdJOaqLs4L/n4559/tDYwjB07lry9vSkmJob27NlDLVq0oMjISLp48SJNnDhR64XZ5MmTycHBgebNm0fjx48nS0tLGjRokFBenCFFk+JGjdKPkpNHiu1DaGgohYWFqf3nkpWVRWFhYaLnwMLCQkgx2bJlSxo5cqRK+YQJEyg4OFhjfPF29DkPoaGhNHDgQMrOzqZ58+aRs7MzDRw4UCjv378/de3aVWP8gAEDqHbt2hQXF6fyni0oKKBdu3ZRnTp1VNZXWseOHemLL74Q/ufNmTOHOnbsSERFk8W6ubnR5MmTNcaXnuiz5EOXc1i8jmnTplHDhg1JIpHQO++8QwsXLtT5/2FISAgNHTqUnjx5QvPmzaMaNWqoZPYYNWoUBQUFaYwvmYHhr7/+IqlUSuHh4bRkyRIaOHAgyWQy2rZtm8Z4ff8X6Lt9Iv1T/3bv3p0CAwPVpstMSUmhoKAgrdlAit27d48WLFhA9evXJ5lMRh06dKCtW7cKEyq/SbiBgTFWHrHn71CzWXvJdex24dFs1l6KPX+nsqvG2FulwhsYHjx4QN27dycXFxcaPHgwFRQU0IABA4QfsoGBgUI2BHXq1q0rXNzOmjWLbG1t6bvvvqPY2Fj6/vvvycHBgebMmaMxPj4+nqysrMjCwoJMTEzI09NTJYuFtru2xZYsWUI1a9Ysc3EVHBysNnVfaTt27KABAwbQ6NGjy9wde/jwIb377rsaY/U9hiV/EB85coQMDQ2pZcuWNHr0aGrbti3JZLIyaRNLKjnXwpw5c3Rq1ChN37vfX375JcXExBAR0c2bN8nLy4sMDAzIwcGBDAwMqF69eqJ330xMTIR5IhQKRZlMHpcuXSJLS0uN8SVnfb979y5FRkaSl5cXSaVSatKkCf3000+UnZ0tGq/pAl3XCzuFQqHS0HPr1i0yNzcXsgZMmzZNNAOBp6encAyJiu6Ge3p6Ur9+/UipVGr9LNSoUYM6d+5M+/fvp4MHD9LBgwfpwIEDZGBgQFFRUcJzmpiYmIg2yJw7d040m4qZmZlwYejg4EAJCQkq5VeuXNEpXao+58Ha2lr4/Obl5ZFUKqUTJ04I5adPnxZNWWtlZSWkvFXn8OHDZGVlpbHc1NRUaGQhIsrNzSVDQ0Ph4v6PP/4QnYukQYMG1LlzZ7p48SJdu3aNrl27Runp6SSTyWjPnj3Cc2JKfhZOnTpFERERZGVlRcbGxtSjRw+VFL7qlMxqk5+fTzKZjM6ePSuUX758WefPYkhIiEoWDiKimTNnimZkqcj/BS+zfSL9U/+am5uXqXdJp06d0vpZUOf06dP05Zdfklwup+rVq9OIESNU3m+VjRsYGGO6ij1/h9xKNCwUP9z+/4MbGRh7fXT9/tZ5woTRo0fj0qVLGDNmDC5evIju3bvj5MmT+Oeff3D48GEUFBRg3LhxGuOvXbsmjHvfuHEjli1bhpEjR6JDhw4YPnw4Vq9ejVWrVmmM//bbb9GtWzc8evQId+/eRdu2bdGyZUucPXtW113A/PnzMXPmTIwePRorVqxA3bp1MWXKFOzYsQO1atVCixYtcOrUKY3xGzduxPvvv4/MzEwcO3YMjRo1woYNG4TyvLy8MnMUlKTvMaQS02VMmTIFffv2xcGDBzF37lzs3r0bQ4cO1Tqb+O7du9GpUyfMnz8fNWvWRFhYGLZv3w6lUikaV8za2rrMw8bGBgUFBQgMDISVlZXofBhbt26Fm5sbAODrr7+Gs7MzMjMzkZmZiXv37sHV1RUjRozQGF+/fn3s378fQNE45dIZFa5fvw4TExOd9sXe3l44FwcPHoSPjw9GjhwJhUKhMaZatWqYPXs29u/fr/bx008/ad3u06dPUaNGDWFZoVAgJycHjx49AgB0794diYmJGuNv374NX19fYdnT0xMHDx7E0aNH0bdvX40ZOIqdO3cOhoaGmD59Ojw9PdGyZUu0atUKEokETZs2RcuWLdGyZUuN8VZWVrh27ZrG8mvXrsHKykpjeUBAAGJiYgAAHh4eZfY1ISEBNjY2ovug73nIy8sT3ieGhoYwNTVVmXuievXqQiYIdZRKpeg8E0ZGRqKfKSsrKzx58kRYfv78OQoKCoR11q9fHxkZGRrj4+Pj4enpie7du+Phw4dwdXUVPldOTk5wdXUt1zwj/v7+WLp0KTIyMrBy5Ur8+++/6NChg+hcGEZGRsjJyQFQdDyVSqWwDBTNMVE8z4Y2ly9fxocffqjyXPfu3ZGSkqIxpiL/F7zM9oGi92Hx++Tx48coKChQed88ePBAdH4hY2NjZGdnayx/8uQJjI2NddkFQUZGBvbs2YM9e/bAwMAAnTp1wvnz5+Hj46MxcwxjjL2JCpWEqTHJUDdZXPFzU2OSUajUeTo5xtjroGuLhUKhEO7YFXfBLnmH6/Dhw6J3/BQKBR07doyIiu5alr5rc/nyZdG7ntbW1nTp0iWV52bPnk3W1tYUHx+vUw8GNzc32rlzp7B86dIlsrW1FcY6Dxs2jNq2basx3s/PjxYtWiQsb9myhczMzGjVqlVEpL0Xhb7HsOQdt5LHs1jxOGRd4vPy8mjLli3COGEnJyf69ttvRcfOE+l/91sul1NaWhoRETk7O6vcNSYiOn/+vOg+bN++nWxsbCgqKoqioqLIzc2NVq1aRUeOHKE1a9aQi4sLjR49WmN8yV4g6mRlZZXJjFFSq1atKDIyUmN5QkKC6PAEIqKgoCCVfPfFXauLnT9/nqytrTXGu7u70969e8s8f/v2bapTpw61bdtWp948S5cuJScnJ9q4cSMREclkMp0yskycOJGsra3pu+++o8TERMrMzKTMzExKTEyk7777jmxsbES79x89epQsLS1p8uTJtHjxYqpevTpNmDCBNmzYQJMmTSIrKyvRY0yk/3nw8vKiffv2Ccvbt2+n58+fC8vHjx8nZ2dnjfGffPIJNWzYUO3d5zNnzpC/vz/17t1bY/ynn35KLVu2pIsXL1JaWpow/0SxgwcPkouLi8b4Yjt37iRnZ2eaNWsWFRYW6nwOibR/FlJTU+nbb7/VWB4WFkbvvfceHT58mAYNGkSNGzemzp0709OnT+nZs2f04YcfUocOHTTGSyQSOnDgACUmJpKrqyvFx8erlKekpIjevdf3f4G+2yci6tOnDwUEBFB0dDR16dKF2rdvT82aNaOLFy9SSkoKtWzZUnSIw5AhQ8jV1ZW2bdumcjcgKyuLtm3bRm5ubvTll1+K1oGo6P/5r7/+Sp07dyZDQ0Py9/enZcuWqaxz27Ztor1qXifuwcAY08XRK/fL9FxQ9zh6pXKHOjP2tqjwIRKmpqYqXW4NDQ1VukmnpaWRmZmZxvghQ4bQe++9RwUFBTRo0CAaOHCgypwL//vf/0S7hVtbW6udtGzevHlkZWVF27Zt03pRZWpqKnRnJSrq0iqTyYRhCQkJCaI/KM3MzISL42L79+8nc3NzWrZsmdYGBn2PoUQioStXrlBWVha5u7uXubi5cuUKmZqaisaru6C4fv06TZ48mVxdXbUewwcPHlDXrl3p3XffVRnKoOuFTf369Wnz5s1EROTt7U179uxRKT969CjZ2NiIruPXX38lZ2fnMmPQ5XI5jRgxQnQeB03HQFc//fSTSiNTaZmZmTRlyhTRdezdu5eMjY2padOm1KJFC5LJZLRw4UKhfN68edS6dWuN8QMGDKDPPvtMbdmtW7fI09NTpwYGIqILFy5QgwYN6OOPPy7XxemcOXNIoVCUSVuqUCi0Ng4QFZ3nZs2alZk/oEaNGvT9999rjdf3PEyZMoU2bdqksfzbb7+lDz74QGP5w4cPqUOHDiSRSMjGxoa8vLzIy8uLbGxsSCqVUseOHenRo0ca4+/evSvsv1QqJVdXV5XP89atW+mHH37QGF9SZmYmdezYkZo3b16uc6jvZ+Hy5ctUu3Ztkkgk5O3tTbdu3aL333+fZDIZyWQysrOzE50os/Q8EiU/A0RFDW9iaXuJ9P9foO/29U39m5OTQ4MHDyYjIyOSSqUkl8tJLpeTVColIyMjioiIEIZOibG1tSVra2saMmSIyjCVkh49evTGpADmBgbGmC7+OHtLpwaGP86+2RPbMvZfUeENDA0aNKAff/yRiIrumlWrVo0WLFgglC9btox8fX01xj9+/JgaN25Mnp6e1LdvX5LL5eTq6kpt27Yld3d3srS0FJ3JvHnz5rRs2TK1ZZGRkWRsbKz1osrPz0/l7vS+ffvI1NRUaOhISUmhatWqaYxX12uAqOhuo7m5OY0fP160Dvoew9IXc6XvtP/555/k6ekpGi92QaFUKrWOuy72sne/o6KiyNnZmQ4cOEDr1q0jb29v2rt3L92+fZv2799P9erVE50cr1hBQQGdOHGCNm/eTBs3bqQDBw6Izp3wpklISKBvv/2Wvv76a52PebFr165RXFycxvLbt2/Tzz//rPP6cnNzaeTIkeTn51emAU2btLQ0Onr0KB09erTcsURFE9MdP36cjh49qtL4V9mePXum04XdxYsXac2aNTRr1iyaNWsWrVmzRphfQheXL1+m8+fPV0g2nUWLFlHXrl3p5s2bOr3+2rVrWifW1UXpSSH37t1LMTExWieLLJ4novhR+vVr167VKZNDQUEBxcfHl/t/QUVtX52rV6+W67xmZWXR/v37aePGjbRx40bav39/uS6+161bRy9evHipulYGbmBgjOmCezAw9mbR9ftbQkQ6DVzasGEDPv30U7i7u+PmzZuIjo7G8OHDERISAqlUim3btuG7777D0KFDNa4jPz8fq1evRkxMDNLS0qBUKqFQKBAcHIyIiAg4OztrjF21ahUOHTqE9evXqy2PjIzE8uXLkZ6ernEdv/zyC/r06YNu3bpBLpdj27Zt+PLLLzF79mwAwIoVK7B27VocPXpUbXzXrl3RoEEDtfMcHDx4EO+99x5evHihcQy8vsew9PwOCoUCderUEZYXLVqEvLw8jB49Wm28u7s7Tp06BVtbW7Xl5ZWcnIxPPvkEPj4+2Lp1KxITE+Hj46M17rvvvsPEiRNBRCgsLERBQYFQ9v7772P9+vWi45YZY4z9n88++wyLFi1CtWrVVJ5/9uwZ/ve//2HNmjWVVDP1dM6jzRh7qxUqCSGR+5GZlaN2HgYJAEdLOQ6PbQ0DqeR1V4+xt46u3986NzAAwJEjR3D8+HEEBgYiKCgIycnJmDNnDp4/f44uXbrg008/rZDKv0qxsbGIjo5Gbm4u2rdvj88//1woK56cS9MF+KFDh3D06FF88803assPHDiAdevWISoqSuP2/wvHsKS8vDyMGzcOBw4cwLZt20QnhSvp8ePH2LNnT5mGptq1a+sUv3//fhw+fBgZGRmQSqWoVasW3n//fZ3iMzIysGzZsjLxXbt2Rb9+/WBgYCAav337dsTHx6N9+/YIDg7G/v37MX/+fCiVSnzwwQcYNGiQ1joQEa5duwYXFxfIZDLk5eXh999/R25uLjp16qQy4aAu6zp48CCuXLkChUKB9u3ba51cT9/t63sML168KHwOvLy8kJKSgkWLFiE3Nxd9+vRB69atte53RZyHYnfu3MGKFSuEYzhw4EB4eXnpHF/ao0ePEBMTg/Dw8Dd2+7du3YJcLhfO9T///IPly5fjxo0bcHV1xdChQxEYGKh1Wy/7WTx9+jT8/f3Lt2OlJCYm4vTp02jVqhVq1aqFCxcuYMmSJVAqlejWrRvat29frvXl5+fj2rVrsLe3h6WlpV51A4C7d+9ixYoVmDRpktryijoHBgYGyMjIgL29vcrz9+/fh6Ojo0oj7puAGxgYY7qKS8pARPQZAFBpZChuTljWpxE6+GqenJsxVnF0/v5+xT0p1Hr8+DGlpKRQSkoKPX78WK91KZVK0XG2b4PMzEy6fv16ueNycnJ06gb+Jrl79y41bdqUpFIpyWQykkql5O/vT46OjmRgYCA6qRsR0cmTJ8nS0pL8/f0pJCSEDAwMqG/fvvTRRx+RlZUVBQUFiXavXr58OclkMvL39ycLCwtav349VatWjQYOHEhffPEFmZiYaJ1DICUlhVxdXUkikZCnpyelpaWRv78/mZmZkampKVWvXl00pVzHjh2Fz82DBw8oICCAJBIJ2dnZkVQqJS8vL7p3757W7Uul0pfavr7HMDY2loyMjMjGxobkcjnFxsaSnZ0dhYaGUuvWrcnAwEBlAkZ19D0PJiYmwjG6cOECWVpakqenJ/Xo0YO8vLzI1NRU7ZwvukpISBAdLlXZ2yciatq0qZDu9I8//iCpVErvv/8+jR07lrp160aGhoYq6VBL0/ezKJFIyMPDg2bOnEm3b98u9z7+9ttvZGBgQLa2tmRubk579uwhKysrCg0NFSav3bBhg8b4yMhIYWLPgoIC+vrrr4W5EGQyGfXv35/y8vLKXa+StJ0Hfc9BcZrgkvPzFD8ePnxIa9euJYVCodc+vAo8RIIxVh6x5+9Qs1l7VYZFNJu1l1NUMvaaVfgcDJqU5wfYypUrydvbu0yuem9vbyETgyb5+fk0fvx4atGiBU2aNImIiObOnUumpqZkZGRE4eHhlJubW6665+fn0+7du2nVqlW0Z88enRsqnj59SocOHaLNmzfTL7/8QqdOnSr3WObr16/T8ePHKT4+XutY5WLZ2dnUu3dvqlmzprC/Q4YMEY5jixYttJ7w3bt3U8eOHcnKyko4D1ZWVtSxY8cyEy5qcvfuXdq3b59wkZuZmUmRkZE0e/ZslRzwunj06BH99NNPNGHCBFq5cqXWBqePPvqIunbtSllZWZSTk0NffvklhYeHE1HRnBq2traiF5bBwcEqk/+tX7+eAgICiKho4j4/Pz8aNmyYxngfHx9h7ov9+/eTXC6nJUuWCOVRUVHk7e0tug9hYWH0/vvv07lz52jEiBHk7e1NYWFhlJeXRzk5OdSlSxfq06ePxviSc2lERESQj4+PMP/BzZs3yd/fnwYPHvzKtq/vMQwMDKTx48cTUdFEetbW1irZCsaNGyeazYVI//NQ8hiGhYVRly5dhPHyhYWF1KtXL3rvvfc0xpe8kFP3+Oeff0QvLCt7+0Sqk9YGBATQnDlzVMoXL16sktmiNH0/ixKJhD7//HOyt7cnmUxGnTt3pt9//13n/8ONGjUSsrEUZ2KZNm2aUD5//nzy8/PTGF8yi8a8efPI2tqa1qxZQxcuXKDo6Giyt7fXOmFpYmKi6GPLli2i50Hfc1ByXh51DwMDA5WMNbqYPHkyoehGofCoW7euUP7ixQsaMmQI2djYkJmZGX3wwQeUmZlZrm1wAwNjrLwKCpV09Mp9+uPsLTp65T4VFOo/hxBjrHwqvIFhy5YtKhfwixcvppo1a5JUKiVbW1uaOnWqaHxxY8C4cePowIEDlJycTMnJyXTgwAH65ptvyMzMjObNm6cxfsKECeTg4EBfffUV+fj40ODBg8nFxYWio6Np7dq1VKNGDa0/Br/88kvhbtDNmzfJy8uLDAwMyMHBgQwMDKhevXoqmRFKKygooNGjR5OpqalKI4lEIiFXV1f666+/RLdPRLRkyRLhuJV8BAcH06lTp7TW38vLi3744Qdq1aoVhYWFka+vLx0+fJgOHTpEPj4+omnlfv75Z5LJZNSrVy+KioqinTt30s6dOykqKoo+/vhjMjQ0pHXr1onW4cCBA2RmZkYSiYQcHR0pISGBnJ2dqXbt2lS3bl0yNjamXbt2aYzv1q0bbd26lYj+L62mnZ0dBQQEkIODAzk6OlJycrLGeAsLC0pKShKWnz59SoaGhsIbff369So/hkszMTGhq1evCsuFhYVkaGgo/EDevXs3OTk5icaX7C1SOhNIenq6aCYPIiI7OzthpvenT5+SRCKhf/75Ryg/cuQI1axZU2N8yYvTunXr0p9//qlSvnfvXnJ3d39l29f3GFpYWAjpUItTK5bMoHD+/HlycHDQGF9cB33OQ8lj6OLiQn///bdK+ZkzZ0Tv/Gq7sCsuf1O3T0RkaWkp9JKwt7cv02NCW1YafT+LxccgPz+ffv31V+rUqZPw/3jMmDFl0hKXZmZmJkwMqlQqydDQUKWB8+rVq6JZgUqeg4YNG9KKFStUyqOjo+mdd94RrUPpTBQlH7qcB33PQXGaYIlEQtu2bRPSBB88eJCOHj36Uj1DJk+eTO+88w5lZGQIj3///VcoL/7u3bdvH506dYqaNWtGQUFB5doGNzAwxhhjVU+FNzCUvNuzZs0aksvlNGnSJNqxYwfNmDGDzMzMaOXKlRrja9asSVu2bNFYvnnzZtG877Vq1RIaB1JTU0kqlQrpDomKGkDEMjAQETk4OAgXIT179qTQ0FDhh9ODBw/ovffeE81ZPnbsWPL29qaYmBjas2cPtWjRgiIjI+nixYs0ceJErRfX8+bNIycnJ1q8eLHQm2PatGkUGxtLffv2JVNTUzp58qTGeBcXF9q/fz8RFWUKkEgkKt1nt2/fLvqDvnbt2kIWC3WWLFkimoWCiCgkJISGDh1KT548oXnz5lGNGjVo6NChQvmoUaNEf2xaW1sLs+x37NiRPvnkE6HhKi8vjwYMGEDt2rXTGG9nZ6eSreL58+cklUrpwYMHRFR0UWFsbKwx3tXVlQ4fPiws37lzhyQSidBVOj09neRyucZ4Z2dn4WKw+Bzs2LFDKD948CA5OztrjCcqe3Fsbm6uksruxo0bovsgkUiE7vX29vYqF3lERbPji8Xru319j6GFhYXK9szNzVUaLK5duyYaT6T/eZBKpcIxdHV1LXNhl5aWpnUfIiMjVS7oSj5WrlwpemFZ2dsnInr//fdp3LhxRETUvn37Mmk/V65cSbVr19YYr+9nUV1Wm1u3btG0adOoVq1aJJVKqXnz5hrjHR0dhUbZhw8fkkQioQMHDgjl8fHx5OjoKLr94nNga2ur0kBFVHQOtDUW2tra0urVq8tkpCh+7NixQ/Q86HsOilVURhCiogaGBg0aqC17/PgxGRoaCo3EREWZVACozbBULCcnR6WHzc2bN7mBgTHGGKtiKryBoeSPwaZNm9LcuXNVypcuXSralVMul4vemb5w4QKZmJiIxt+4cUNluWQ6uLS0NNEUk8Uxxd1RnZ2d6cSJEyrl58+fp+rVq2uMVygUKncab926Rebm5sI8BtOmTaPAwECN8W5ubrRz505h+dKlS2Rrayt0jR42bJho13BjY2OVY2Bqaqpyl+/atWuiP4iNjY0pJSVFY3lKSorWC7uSF4f5+fkkk8lU8q5fvnyZLC0tNcabmJgI8QqFQuXONVHRMRGL79atG3Xv3p2ePn1KeXl5NGLECJVGkePHj4teVAwfPpx8fX0pNjaW9u/fT++++y61atVKKI+LiyMPDw+N8UOHDqXatWvTjBkzqGnTpvTpp5+Sl5cXxcbGUlxcHNWrV48+++wzjfFERB4eHio9BpYuXaoyZ8Hp06e1Xhh16tSJunXrRtbW1mXGaB8/fly0B4C+29f3GNavX59iY2OF5dLp/P7++2/RHhhE+p8HiURCVlZWZG1tTYaGhrR+/XqV8t27d5Obm5vG+FatWon2mEpISCCJRPLGbp+IKDk5mWxtbSk8PJymT59O5ubm1KdPH5o5cyaFh4eTsbExRUVFaYzX97NYstFanb1799Inn3yisbxPnz4UEBBA0dHR1KVLF2rfvj01a9aMLl68SCkpKdSyZUvRBmOJREIzZ86kRYsWkUKhoEOHDqmUJyYmkrW1tcZ4IqJ27drR9OnTNZZrOw/6nANtwzNKPspj8uTJZGpqSgqFgtzd3emTTz4RGiT37dtHAOjRo0cqMTVr1qTvvvtOdJ2lh11wAwNjjDFWtejawCArz8yREknRnK1paWlo166dSlm7du0wduxYjbFNmjTBnDlzsHr1ashkqpstLCxEZGQkmjRpojHe0tISjx8/houLCwCgUaNGKim5cnNzhfppUqdOHcTHx8Pd3R3VqlVDdna2SvmTJ0+gVCo1xj99+hQ1atQQlhUKBXJycvDo0SM4Ojqie/fumDNnjsb4e/fuwdvbW1iuXbs2srKy8O+//0KhUOCzzz5DSEiIxnhbW1v8+++/wjEICwuDlZWVSv2MjY01xr/zzjtYvXo15s6dq7Z8zZo1WtNMGhkZIScnB0BRBgmlUiksA8CLFy9EMxjUr18f+/fvh4eHBxwdHXH9+nU0bNhQKL9+/TpMTEw0xs+fPx/t2rWDlZUVJBIJzMzMsHXrVqH84sWL6Nevn8b4GTNmICMjA126dEFhYSECAwMRHR0tlEskEiFtqTqRkZHIy8vD5s2bERQUhMWLF+OHH35AWFgY8vPz0bJlS9F4AAgNDUVKSopwriMiIlTKd+/ejUaNGmmML5lpJCwsDM+fP1cp/+233+Dn5/fKtq/vMYyIiFBJ5err66tSHhsbqzWLhL7noXSmF09PT5Xl48ePo1u3bhrjP/nkE7x48UJjuaOjIyZPnvzGbh8AvL29ceLECUyYMAFz587Fs2fPsGHDBshkMjRp0gSbN29G165dNcbr+1kkLQmM2rRpgzZt2ohuv2/fvhg8eDCCg4OxZcsWTJgwAT4+PpBIJPDw8MDq1as1xtesWRMrV64EABgbG+PMmTNo0aKFUH7gwAHUrVtXtI6DBw/Gs2fPRLchllVIn3Pg5+cHiUQCItL63acpdbI6AQEB+Pnnn1G3bl1kZGRg6tSpaN68OZKSkpCZmQkjIyOV7x0AcHBwQGZmpsZ1fvPNN/jqq6+E5ezsbOF7jDHGGGP/Mbq2WEgkElq3bh39+eef5OzsTEePHlUpT0pKIgsLC43xiYmJ5OjoSLa2ttStWzcaPHgwDR48mLp160a2trakUCjKdFEt6d1336Wff/5ZY/kvv/xC/v7+ovsQFRVFzs7OdODAAVq3bh15e3vT3r176fbt27R//36qV68eDRw4UGN8UFCQyoRZxROLFTt//rzoHS8/Pz9hYjqiortBpqamQtfWlJQU0V4YHTp0oOXLl4vun9jwhOL5E+rVq0cjR46kOXPm0Jw5c2jkyJFUv359Mjc3L3MXr7SwsDB677336PDhwzRo0CBq3Lgxde7cmZ4+fUrPnj2jDz/8kDp06KAxfvv27WRjY0NRUVEUFRVFbm5utGrVKjpy5AitWbOGXFxctM4+/+zZM9q1axfFxMSojA0ujxcvXtCTJ09eKlbT+sQyJ5RHWloa3bnz8jMjP336lF68ePHKt1/Rx7AiVOR5eJsolUrKzMykO3fulGviXn0+iwcPHlTpuVJRrly5UqZXzMs4duxYmR5Wr1J5z0HJoRi///47eXh40PLly4VeC8uXL6fatWvT77//rle9Hj16RBYWFrRq1SrasGEDGRkZlXlNkyZNaMyYMTqvk+dgYIwxxqqeVzJEouSj9MzUq1atEh0iQVSUBWHp0qUUHh5O7dq1o3bt2lF4eDgtW7ZMa0UvXbokDG9QZ8OGDaJzPBRbsGABmZqakomJiZCSrPjRtWtX0QumvXv3krGxMTVt2pRatGhBMpmMFi5cKJTPmzePWrdurTF+y5YtZGhoSD179qTw8HAyNzcXxt8SFaXeExti8eDBgzJdU0vauXOnyhhkddLT02nMmDHUokULqlOnDtWpU4datGhBY8eOFSZME3P58mWqXbs2SSQS8vb2plu3btH7779PMpmMZDIZ2dnZ0enTp0XX8euvv5Kzs3OZydHkcjmNGDGiUtKOHjhwQJhDgL0cPoYvp6CggDIzM0VTizLdVdRcBFVJkyZNVOYgKbZjxw5q1KiR3utv3LgxjRs37qWHSJTGDQyMMcZY1aPr97eESEs/VR1t374dhoaGaN++fUWs7pV6/Pgx9uzZg7S0NCiVSigUCgQHB6N27dpaYxMTE/HLL78gNzcX7du3R9u2bcu17djYWERHRwvxn3/+uVD24MEDAEVDId50Dx48UKnnvn378OLFCwQGBupU/8LCQpw5c0blHPj7+6sMe9Hk4sWLOH78OAIDA+Hl5YWUlBQsWrQIubm56NOnj9bu9eoYGRkhMTFRZQiLJhkZGVi2bBkOHz6MjIwMSKVS1KpVC127dkW/fv1gYGCgdR3379/HmjVrcOzYMaFrsaOjI4KCgtCvXz/Y2dlprcO+fftgY2OD0NBQGBkZCWXPnj3DggULMGnSpFe2fXXKcwxXrVqFf/75B61atUL//v2xZcsWTJkyBbm5uejbty+mTp1arm3fuXMHK1aswJUrV6BQKDBw4EB4eXlpfH18fDz8/f2Fc7V9+3bMmzdPiB82bBjCw8NFt6nv+3DHjh2IjIxEfHw88vPzAQDVqlVDly5dMHPmTNSsWVNj7K1btyCXy1G9enUAwD///IPly5fjxo0bcHV1xdChQxEYGCi6fQD48ccfER8fj06dOqFXr15Yv349Zs+eDaVSiQ8++ADTpk0rM6StJH3eRwsWLMCHH34IV1dXrfUsj/K8Dx88eIBz586hQYMGsLGxwf3797F69Wrk5uaiR48eOq1DzM2bNzF58mSsWbNG42v0/SwDgImJCc6cOVOmvhcvXkSjRo1Eh9No8/TpU9SsWRNTpkzBp59+Cjs7O2zatAndu3cHAFy6dAleXl44duwYmjVrptM6s7OzYWlpiaysLFhYWLx03Rhjr1ehkhCf/hD3nuTAvpocTd1tYCAVH6LFGPvv0PX7u8IaGHRVWFiocgF24sQJ5ObmIjAwUHTsvjYFBQW4c+eO6I/y/6rWrVsjKirqpX+o5+fn63XsX6e4uDiEhYXB3Nwcz58/x++//47w8HA0aNAASqUShw4dwu7duzVe3GmaWyAhIQFeXl6Qy+UAgDNnzqh93alTpxAaGgpPT0+YmJjg2LFj+OSTT5CXl4ddu3bBx8cHcXFxog0lJ0+eRPv27WFqaorQ0FA4ODgAAO7evYt9+/bh+fPn2LVrFxo3bqwxvl27dlAqlcjPz0eNGjXwxx9/4J133hHW4+TkpHHctb7b1/cYfv/995gwYQLat2+PY8eOYejQoVi4cCFGjhyJwsJCLFiwAPPmzcOgQYM0HEHA1NQU169fh52dHZKTkxEUFAQ7Ozs0bNgQ58+fx40bN3Ds2DHUr19fbbyBgQEyMjJgb2+PmJgYdO3aFX369EFAQADOnj2Ln3/+Gb/88ovGeRD0fR+uX78eQ4cOxaBBgyCXy7F69Wr069cPrq6u2Lx5My5cuICjR49qbPQMCAjAxIkT8d577+HPP//EBx98gPfeew/e3t64fPkytm/fjm3btuG9997TeAxnzJiBuXPnol27djhy5AhGjBiBefPmYeTIkZBKpVi4cCEiIiI0Nvbo+z6SSqWQSqV49913MXDgQHTr1k3l4lqbkmP6S1q0aBH69OkjNHR+9913al8XHx+Pdu3aITs7G1ZWVtizZw969OgBmUwGpVKJO3fu4PDhw6LzkWiTmJiIRo0aiX4W9fksF2vUqBF8fX2xatUq4Rjm5eVh4MCBSEpK0vhZVGfUqFHo0qULXF1dcefOHUyePBkJCQlITk6GnZ0dIiIisHPnTvz888+wsLDA//73PwDA0aNHdd4GNzAwVvXEJWVgakwyMrL+b94thaUck7v4oIOvohJrxhh7XXT+/n7ZLhKPHj2in376iSZMmEArV66kx48fi77+zp07FBwcTAYGBtSiRQt6+PAhde7cWegeX6dOHb3GnSckJGhNy6bN06dPtc5BUBHxpYcAHD9+nA4dOqR13O2ff/6p9mFgYEA//vijsKzJli1bhJSQRESLFy+mmjVrklQqJVtbW5o6darWuhMVHevVq1cLqQWTkpIoIiKCvvjiC4qLixONzcnJUdnPK1eu0Lfffkt9+vSh8ePHiw6DISIKDAyk8ePHE1HRHBjW1tb07bffCuXjxo0TzcQhk8moQ4cONGXKFOExefJkkkqlNGTIEOE5TYKDg1XK169fTwEBAURUlCrPz8+Phg0bJroPAQEBNGjQILVduZVKJQ0aNIiaNWumMT40NJT69+9PhYWFlJ2dTREREWRrayuMF8/MzBT9LOi7fX2PoZeXF23YsIGIiM6cOUMymYxWrVollK9atUrrfCols9qEhYVRly5dhDH3hYWF1KtXL3rvvfd0ig8JCVEZqkRENHPmTNFjoO/70MvLSyXN7smTJ8nZ2Vk4Jx999BF169ZNY7yZmZnwWQkICKA5c+aolC9evFjrkDUPDw/67bffiKjoM21gYEDR0dFC+bZt20TT1ur7PpJIJBQVFUVhYWFkaGhItra2NHz4cNG5eErH+/n5UatWrVQeEomEmjRpQq1ataJ3331XY3xoaCgNHDiQsrOzad68eeTs7KwyB0///v2pa9euonXQ9D+5+LFw4ULRz6K+n+ViJ06cIHt7e7Kzs6M2bdpQmzZtyM7Ojuzt7ctkS9Lmo48+IoVCQUZGRlSjRg366KOPVNLKvnjxgoYMGULW1tZkampK3bp1o4yMjHJtg4dIMFa1xJ6/Q25jt5NrqYfb/3/Enn/53++Msaqjwudg6Natm5D7OikpiapXr052dnYUEBBADg4O5OjoKJqGsm/fvhQUFER//fUXffTRRxQUFETNmzenW7du0fXr1yk4OJiGDh2qa3XKqIgGBn3XoS1e30YWiURSZt6C0g+x7ZdMC7dmzRqSy+U0adIk2rFjB82YMYPMzMxo5cqVovv422+/kYGBAdna2pK5uTnt2bOHrKysKDQ0lNq3b08GBgbCxaM6LVu2FN5Hhw8fJmNjY6pfvz599NFH1LBhQzI1NS0zgWhJFhYWlJqaSkRFF5IymUxlIrbz58+Lpmg8fPgweXh40KRJk6iwsFB4XiaT0YULF0T3nagozWZxw0pxHQwNDSkzM5OIitILOjk5ia6jdIrV0i5evCiaLtTa2lolPSkR0ezZs8na2pri4+O1XpTou/2KOIbFae+IitKnJiUlCcupqakqk6eqU7KBwMXFRSV9LFFRw4VCodAp3t7enk6dOqVSnpKSIloHfd+HJiYmZeY8kclkdPv2bSIqumAU276lpaWQftDe3r5MKsIrV66IpqwtrkPJ82BoaKhyHrSlvdX3fVTyHNy9e5ciIyPJy8uLpFIpNWnShH766SfRCTtnz55N7u7utG/fPpXndX0fWltbC99ZeXl5JJVKVS7GT58+TTVq1BBdh77/k/X9LJf09OlTWrFiBY0cOZJGjhxJP/30Ez19+lSn2NeNGxgYqzoKCpXUbNbeMo0LJRsZms3aSwWFb9/8N4y9bXT9/pbq2iXi4MGDQjq50aNHo127drh16xaOHz+OmzdvonPnzhgxYoTG+L1792LBggXo0qULli5dimPHjmHy5MmoUaMGatasiWnTpiE2NlZjfKNGjUQfvXr10nVXKs3YsWNBRPj999+hUCjw3nvvITs7Gzdv3sS1a9dgZ2eHmTNnaoxv3749OnbsiMzMTCiVSuFhYGCApKQkKJVK0a60VGI0zPLlyzFt2jRMnToVnTp1wvjx4zFv3jwsXbpUdB9mzpyJqVOn4v79+1i5ciV69OiBr776Cnv27EFcXBwiIyMxb948jfFnz55FgwYNAADjx4/HkCFDkJiYiM2bN+PMmTP46quvMHr0aNE6FKdkk0qlkMvlsLS0FMqqVauGrKwsjbHBwcE4ffo0Ll++jKCgIFy9elV0W6XZ29sjIyNDWL579y4KCgqEbkK1a9fGw4cPRdfh6OiI+Ph4jeXx8fFCd3NNSqYGBYBx48bh22+/Rbt27bR2VdZ3+/oeQ1NTU5XUfnZ2djA3N1d5TUFBgeg6JBKJyvug5HsAAKysrPDo0SPRdSQnJ+PcuXMwMTFRm55WlzoUb7+870M3NzecOnVKWD5z5gykUqlw3G1sbIR5GdRp2bIlNm3aBABo2LAhDh48qFJ+4MABlZS66jg6OiI5ORkAkJqaisLCQmEZAC5cuAB7e3vReH3fx8Xs7e0xZswYXLx4EQcPHoSPjw9GjhwJhUJzt9tx48Zhy5YtiIiIwKhRo0SPlzp5eXlCSlxDQ0OYmpoKc1oAQPXq1YV5cTRRKBTYtm2byv/jkg9dhibo81kuyczMDIMGDcJ3332H7777Dp9//jnMzMx0jmeMMXXi0x+qDIsojQBkZOUgPl38tw9j7O2hefauUnJycoRx+gkJCdixY4cw1tPQ0BBjxoxB06ZNNcY/evRI+MFrY2MDU1NTlTkDPD09VS7cSktOTkavXr3g7u6utjwjIwOXL18W3QcbGxvRcm3jXPWN37t3L7Zt24ZmzZohODgY1atXx549e4TjMm3aNJVJH0uLjY3FwoUL0bhxYyxdulR0fLUmxRdFaWlpaNeunUpZu3btMHbsWNH4S5cuoXfv3gCAjz76COHh4Sp52rt164YpU6ZojC8sLBSOU/GkeCX169cP33//vcZ4Nzc3pKamwsPDAwBw7NgxlXk3bty4IXpRAgCWlpbYtGkToqKiEBISgqlTp2rNI1+sa9euGDx4MObNmwdjY2NMnz4dLVu2FC5ULl26pPXCbtSoURg0aBBOnz6NNm3alBm7vnLlSsyfP19jvK+vL44ePVpmfoFRo0ZBqVTi448/fqXbB/Q7hl5eXjh37pwwId3NmzdVylNSUuDm5ia6DiJCnTp1IJFI8PTpU5w7d07leFy5cgWOjo6i62jTpo3Q6HbkyBE0adJEKDt79qzofC76vg+HDh2KgQMH4uTJk5DL5Vi1ahX69u0rzE9z4sQJ1KlTR2P8nDlz0Lx5c9y5cwchISEYP348Tp48CW9vb1y6dAlbtmzB8uXLRfe/d+/eCA8PR1hYGPbt24cxY8Zg1KhRePDgASQSCWbOnIkPP/xQY7y+7yNN75fmzZujefPm+OGHH7BlyxbRfWjSpAlOnz6NoUOHonHjxtiwYYPO70MXFxekpaUJ77XNmzernLOMjAyVBgd1/P39cfr0aYSFhaktl0gkKg27pen7WWaMsVft3hPNjQsv8zrG2H+fzg0M9evXx/79++Hh4QFHR0dcv34dDRs2FMqvX78uXGSpU3zn18XFBQDw5ZdfqlywP3r0SPRui6+vLwICAhAREaG2PCEhAStXrhTdh9zcXERERKBevXpqy69fvy46e72+8fo2sgDAyJEj8e6776J3796IiYnBwoULRV9fWlxcHCwtLSGXy/H8+XOVspycHK0/zqtVq4YHDx7Azc0Njx8/RkFBgcpdvgcPHpS5G11SQEAAYmJi4OXlBQ8PDyQmJgo9GoCi8yjWkBMREaHSkFPcq6ZYbGyszlkk+vfvj5CQEPTu3Vvr3epiM2bMQEZGBrp06YLCwkIEBgYiOjpaKJdIJJg9e7boOoYOHYrq1atj4cKFWLp0qbA/BgYG8Pf3x88//4yePXtqjA8PD8ehQ4cwePDgMmVjxowBEYleXOq7/ZJe5hhGRkaKftZv3LiBL774QnQdUVFRKsuenp4qy8ePH9c4QSMApKenqyyXfs/m5eWJNrbp+z4cOnQopFKpkFGmX79+mDhxolDetGlTbNy4UWO8t7c3Tpw4gQkTJmDu3Ll49uwZNmzYAJlMhiZNmmDz5s0qDX/qTJ06VZio9PPPP8e4cePQoEEDjBkzBs+fP0eXLl0wffp00X3Q530kduENABYWFqINrsXMzc2xdu1abN68GaGhoVobeov16tUL9+7dE5Y7d+6sUv7XX3+JNpoDRb35SvbGKc3T0xMHDhzQWK7vZ5kxxsqrvJkg7KvJdVqvrq9jjP336ZxFYseOHQgPD8eCBQsAFP04nTBhgnDHbPLkyejVqxfmzp2rNj4sLAytW7fG8OHD1ZYvWbIE27Ztw759+9SWDx8+HBKJROPd7atXr2LgwIGiP+aCg4PRs2dPjXXQNuO3vvGurq7YunWr8KN13LhxGDNmjHBBnZiYiNDQUPz7778a96HYixcvMHLkSOzfvx9paWk4d+4cfHx8RGOkUtURMdOnT8f48eOF5dWrV2PJkiWi3Xr79u2L1NRU/O9//8OWLVuQl5eHrKwsREVFQSKR4IsvvoCdnR22bt2qNv7YsWPo2LEjRowYgerVq2Pq1KkYPHiw8D764Ycf8M0332DMmDFaj0FFUSqVePLkCSwsLHS++5mTk4OCggLRxhRd5Ofn4/79+wCKumS/7mweFbX9lzmGrGIQEe7duwelUlkp7yGg8t/HxW7duoXTp08jNDRU7+EBz58/h4GBAYyNjSuodqwYZ5FgrHK8TCaIQiUhJHI/MrNyoO6CQQLA0VKOw2Nbc8pKxv7jXkmayt9++w0jRozAnTt3VO4+GRsbY/DgwZg/f75KCsryiI+Ph6mpaZk7gRVp1qxZyM/Px+TJk9WW37x5E5MmTSpzd7Si4vVtZFHnr7/+woEDB/DNN9+IjpfWxfbt22FoaIj27dtrfM3du3fRt29fHDt2DMHBwdiyZQsmTJiAJUuWQCKRwMPDA7GxsULXcXWOHTuGr776CidOnFB53snJCaNHj9Z4fBhjjFV93MDA2OsXl5SBiOgzZRoJipsElvVppLGRoTgWgEq8LrGMsf+OV9LAABSNoT9z5gzS0tKgVCqhUCjg7++PatWq6V3pt93raGR5VdLS0vD8+XN4eXlBJtNt5M2///6r8j7SNu5eF0uXLsX9+/cxadKkl4r/9NNPcfPmTezfv79Stl8R6/j222+RmZmJNWvWVMr29T2G+sYD+h+D0NBQpKWlIS0t7aXiK/sY/vnnn8jKykJ4ePhLxQP674O+8fruQ2VvvyLqoO/7+E3FDQyMvV7FvRA0TdaoSy+El+n9wBj7b3llDQyvSkZGBvLz80UnVmPiTp06hefPn6NFixaVXZVK06ZNG6Snp7/0heG3336LjIwMjb1QXvX2K2Id+l6cVvYx1Dce0P8YLFmyBPfv39fYW0mbyj6GXl5eQmaIl6XvPugbr+8+VPb2K6IOFdHY5u7ujtatW2P69OlwcnJ66fVUJG5gYOz1Onb1AT5eeVzr6zZ93gyBHrYay8s7fwNj7L/ltTcw6NtA4O3tjcuXL7/0jzl94wH996Gy49+EY6hvIwc3kjDGWMWZMmUKrl27hkOHDpWZ3LSycAMDY6/Xnwm3MXxzgtbXLerlhzA/8UxYjLG3l67f3zpnkdCmdevWel2crlu3rkxWg/KYPXu2aN55Xei7D5Udv2/fvnLngi+pIo5h37599doHfeMZ0xcR8USV7D9DLG0wY+ztwJkgGGOvk1T7S3Szbt06vbpxNmnSBC1btnzp+K5du+L/sXfn4TFe7//A3zPZ90iIJCIRa8S+RmInBCmhKGqvpTQo+lH72lpKW0VbStVStbRqb4WQ2PcQ+y4oEkvJJrLO/fvDL/M1TWYykcQkvF/XNddlnrM893MyMpl7znNO3759X7s9kPdrMHR7V1dXjW0vcys/xnDv3r15uj3gdduXLVsW169ff+3zZvrnn3/w0UcfFej5v/nmG9y5cyfX53jVjh07MGXKFBw+fBgAEBYWhnbt2qFNmzZYunRpgZ9fl9cdw9y0Hz58OA4ePPja50hJScH//vc/NGnSBF999RWAl1uQWltbw8bGBh9++CHi4+Nz3W9uXgfff/89+vTpg/Xr1wMAfv31V3h7e8PLywsTJkzQe9vPV7Vo0SLffrbPnj3D6tWrtZanpKRoJDRv3ryJiRMnonfv3pg0adJrfVseFRWF0NBQXLhwQe829+7dQ2JiYpbjaWlpOHDggNZ2f/75Z56S2vkRAwA8efIEc+fORadOneDr6wtfX1906tQJ8+bN02tHoVelpqbi6tWrr/XaIaK3V31PB7jYmUNb6lyBl+sp1PfUvk04EZG+3tgaDE+ePEHx4sXfxKneWhEREahTp46hwzCohQsXZnt89OjR+Pzzz+Hs7AwAGDFixGv1n9NWo/lxfqVSCaVSiebNm2PgwIHo1KkTTE1N9Y7xp59+wrBhw1CjRg1cv34dP/zwAz755BN069YNRkZGWL16NWbPnq11N468nj8nOY1hfrRXKpXqXUsGDBiAvn37qsdeH6NHj8aGDRvQo0cP/P3332jevDl27NiBWbNmQalUYsqUKWjbtq3Wn3deXwdffvkl5s6di9atW+Pw4cMYOXIk5s2bh1GjRkGpVGL+/PkYOnQopk+fnm37bdu2ZXv8/fffx4IFC1C6dGkAQIcOHXSOgy45/RyaNWuGYcOGoUuXLjh8+DBatmyJSpUqqW+1unr1Kvbs2QNfX99s23/yySeYO3curK2t8eLFC/Tu3RubN29WzyBp2rQptm3bpnUr2OjoaAQFBSEiIgIKhQIffvghfvzxR3X9hw8fwtXVVWv8SqUSNjY26NatGwYMGAAfH59cj1FeYzh58iQCAgJgaWkJf39/lCxZUt1u7969SEpKwq5du1C3bl2dcSQlJWH48OFYtWoVAODatWsoW7Yshg8fjlKlSmHcuHG5vraCxFskiN487gRBRHn1RtZguH79Ou7evQsPDw+UL19eZ10jIyM0a9YMAwYMQOfOnV9rb/G///4bmzZtgoODAz766CN4eXmpy549e4bOnTvnaQbA60pLS3vtfd9zM4ZKpRJly5bFRx99hH79+uV6wa4TJ06gTp066q1Ed+zYgXnz5uHGjRtwcXHBiBEjcr1ienp6OsLDw9XX0Lx5c722Kn3+/DkiIiIQHR2tvq7atWvnODVdqVSiVKlSWXaquHPnDlxdXWFiYgKFQqF1FoS2D2aZbt26hc8++0znh5K8nD+zj19++QVbtmzB33//DVtbW/Tq1QsDBw7UaweRKlWqYOTIkRg0aBDCw8PRrl07fPPNN/jkk08AACtXrsTcuXNx6dKlAjl/Xscwr+0zryE0NBTbt2/Hb7/9hri4OLRt2xaDBg1Cu3btoFTqnpzl7u6OX375Rb1bRIUKFbBp0yYEBQUBAEJDQzFo0CDcvn1b6/nz8jooX7485s6di/fffx9nz55FnTp1sGrVKvTs2RMAsHnzZnz++edaZ0NkJlh0/fpWKBQ6xzCnGRrnzp1D06ZNtfZhZ2eHU6dOoUKFCmjWrBlq166Nb7/9Vl0+efJkhIeH49ChQ9m2NzIyQnR0NJycnDBhwgT8+uuvWL16NXx8fHDmzBn07dsXXbt2xezZs7Nt37dvX1y9ehXff/89YmNjMW7cOCgUCuzevRvFihXDw4cP4eLiApVKlW17pVKJ6dOnY/PmzYiMjIS3tzcGDhyI3r17w9FR+yJn+RlDgwYNUKNGDSxZsiTL7z4RwZAhQ3Du3DkcPXpUZxyffvopDh8+jO+++w5t2rTBuXPnULZsWWzduhXTpk3DmTNn9LqeN4UJBiLD4E4QRJQXer9/i55mzZole/bsERGRp0+fSsuWLUWhUIhCoRClUilt2rSRZ8+eaW2vUCikTZs2YmpqKsWKFZNhw4bJmTNn9D29/Pbbb2JkZCSBgYHSqFEjMTc3lzVr1qjLY2JiRKlU6t1fdiIjI3X2sWHDBklJSVE/X7Rokbi7u4tSqRRHR0eZPn26zv7zYwwHDRokTk5OYmxsLIGBgbJ582ZJT0/X6/qUSqU8fPhQRES2bdsmSqVS+vTpIz/88IMMHDhQjI2NZdOmTTr7GDZsmGzfvl1ERP755x/x8vISIyMjKVmypBgZGUm1atXk3r17WttnZGTImDFjxNLSUpRKpSiVSvUYeHh4yLZt23Se/+OPP5aaNWvKpUuXNI4bGxvLxYsXcxyDzLHOPGd2D12vgbyePzOGzJ/Dw4cP5auvvhIvLy9RKpVSr149Wbp0qcTHx2ttb2FhIXfu3FE/NzExkfPnz6ufR0VFiaWlZYGdP69jmNf2/72G1NRU2bBhgwQEBIiRkZG4urrKhAkT5Pr161rbZzeGFy5cUD/PaQzz+jrI6fy3b9/Wef42bdpIYGCgegxye36R//s5aHvk9HOwsrKSy5cvi4hIyZIlJTIyUqP8xo0bYm1trfP8mfFXrVpV1q5dq1G+detWqVixotb2rq6ucvz4cfXz5ORkad++vdSsWVP+/fffHN8TXj3/qVOnZOjQoWJvby9mZmbStWtX2b17t9a2+RWDubm5egyzc/nyZTE3N88xDnd3dzl69KiIiFhbW8vNmzdFROT69etiY2OTY/s3LS4uTgBIXFycoUMheuekZ6jkyI0nsuXMPTly44mkZ6gMHRIRFRH6vn/rnWBwc3OT06dPi4jIwIEDpVatWnL69Gl58eKFREZGSoMGDWTAgAFa22f+Mff48WP5+uuvxdvbW5RKpdSuXVt+/PHHHAOtWbOmLFiwQP18w4YNYmVlJT///LOI5F+CQaFQaC1/9QP6L7/8Iubm5jJlyhT566+/5MsvvxQrKytZtmyZ1vb5NYZpaWmyceNGadeunfrD/eeffy5Xr17VeX2v/kHdqFEjGTdunEb5zJkzpUGDBjr7KFmypPrD7AcffCD+/v7y+PFjERH5999/5b333pMuXbpobT927FipXLmybN++XUJDQ6VJkyby1VdfyeXLl2Xy5MliZmYmu3bt0hnDpk2bpHTp0rJo0SL1MX0/WLm6usqWLVu0lp85cybH11Fezi+i+XN41YEDB6Rv375iZWUlVlZWWtu7ubnJgQMHRETk/v37olAo5K+//lKX79u3T9zc3Ars/Hkdw/z4GWi7hjt37sjUqVPFw8NDZx+VKlWS9evXi4jIiRMnxNTUVH755Rd1+fr166VChQo6Y8jL68DT01N27twpIiLXrl0TpVIpv//+u7r8r7/+kjJlyujs49tvv5XSpUurE365Ob+IiK2trXz11Veyb9++bB/Lli3TOYYtWrSQuXPnioiIn5+frFq1SqN848aN4u7urrW9QqGQR48eiYhI8eLFNRIsIi+TLBYWFlrbW1lZybVr1zSOpaWlSceOHaV69epy7tw5vRMMmV68eCGrV6+WZs2aiVKpzPFnkNcYypQpk2XcXrVq1Srx8PDQGYPIy4RVZlLh1QRDZGSk2Nra5tj+TWOCgej1MUFARIaS7wkGMzMzuX37toi8/KNo//79GuWnTp0SFxcXre2z+2PuyJEj8tFHH4mNjY1YWlpK7969tba3srKSW7duaRwLCwsTa2trWbx4sV4Jhk6dOul8tGjRQu8/SOvXr6/+4zrTjz/+KLVq1dLaviDG8N69ezJjxgwpW7asKJVKady4sV7tnZyc5NSpUxrlV65cEXt7e63tRV5+45b5c3Bzc9P49k5E5Pz581K8eHGt7V1cXNQfjjPjt7a2luTkZBERmTFjhvj6+uqMIbNdixYtpE2bNhIdHa33B6v27dvL5MmTtZbnlGTK6/lFNBNV2YmLi5OlS5dqLQ8ODpYKFSrIl19+KfXr15e+ffuKl5eX7Ny5U0JCQqRatWry0UcfFdj58zqG+fEz0JZgyKRSqXR+Az1//nwxNzcXf39/KVasmCxcuFCcnZ3l888/l3HjxomdnZ3MmDFDZwwir/86mDRpkpQoUUIGDhwonp6eMm7cOHF3d5fFixfLkiVLpHTp0jJq1Kgc+zlz5ox4e3vL4MGD5fnz57l6HTZr1ky++uorreU5/RyOHDkidnZ2MnXqVFm0aJEUL15cJk2aJL/99ptMmTJF7O3tdfavUCjk448/llGjRomTk1OWn1dERITO3yXVqlWTjRs3Zjme+QE/c3aZNjn9P7h+/bpMmDBBa3l+xPD999+LmZmZjBgxQrZu3SrHjh2TY8eOydatW2XEiBFiYWEhP/zwg84YREQaN24sCxcuFJGXCYbM39HDhg2TgICAHNu/aUwwEL2enecfSINZe8Rj7A71o8GsPbLz/ANDh0ZE74B8TzBUrFhRduzYISIvv307fPiwRvmZM2d0flOi64+5xMRE+fnnn8XPz09rexcXF/UU0Fft27dPrK2tZeLEiTkmGIyNjaVt27bSr1+/bB8dOnTIMcHw6jdu2U0J1jUdtSDHUERkz5498uGHH+qMPzw8XM6ePSseHh5y4sQJjfIrV67onNIsIlK9enX1N7+VK1eW0NBQjfIjR46Ig4OD1vY2Njbqb9dEXt4yYWxsLNHR0SIicvHiRZ1Tw1+lUqlk1qxZ4uzsLEZGRnp9sDpw4ID6m+PsJCYmyr59+wrs/CI5fzjOSWJiogwaNEiqVq0qgwcPlpSUFJk3b56YmpqKQqGQZs2a6ew/r+fP6xjmx8+gTJky8uTJk5yD1eG3336TYcOGqafmh4eHS+PGjaVOnToybdo0ycjI0Kuf13kdZGRkyMyZM+W9996TWbNmiUqlknXr1knp0qXF0dFR+vXrJ4mJiXqdPykpST7++GOpUKFCrl6HS5cu1ZgV9l8xMTEybdo0nX0cOXJEGjRokOUWl1KlSsl3332ns23Tpk2lWbNm6sd/Z3998cUX0rRpU63tP//8c2ndunW2ZWlpaXr9Ps/L/4P8iEHk5WwZHx8fMTY2Vo+fsbGx+Pj4yIYNG/SK4+DBg2JtbS1DhgwRc3Nz+fTTT6VVq1ZiZWWVJZFcGDDBQJR7O88/kDKvJBYyH2X+/4NJBiIqaPmeYJg3b55UrlxZrl+/Lt988434+vrKjRs3RETk1q1b0qxZM51T4/P6x1xQUJBMmTIl27Lw8HCxsrLK8Q+5atWqqW+pyE5OU7MVCoWsXr1atm7dKm5ubnLkyBGN8gsXLuhMEBh6DP977/v8+fM1ytetWyfe3t46+1ixYoW4ublJeHi4rF69WipXrix79uyR+/fvS1hYmFSrVk0GDhyotb2fn598+eWXGud8ddbE+fPnpVixYrm6rlOnTsl3330nT58+zVW7/GLo82d68eKFzrUTqGAZ+nWwdetWGTlyZJ4/NL+OR48eybFjx+TIkSMSFRWVL33evHlT/vnnH63laWlpOt/g0tLS1DPGsnP79m1RqfI2tTivMbwqNTVVHjx4IA8ePJDU1NRcx3Ljxg0ZOHCg1KtXTypXriw9e/aUc+fO5bqfN4EJBqLcSc9QZZm58N8kQ4NZe3i7BBEVKH3fv421Lf74X//73/9w9+5deHt7o1y5crh9+zYqVqwIY2NjpKeno3bt2li3bp3W9itWrICdnZ2+p8ti1KhROHLkSLZlzZo1w/bt23Xu2Q4AderUwenTpzFgwIBsy83MzODu7q6zj759+6r/HRYWprEF27Fjx1CuXDmtbfM6huHh4XBweP09iv+7L/1/t39LTU3F2LFjdfbRr18/PH36FIGBgRARZGRkoHXr1uryDh06YP78+Vrbz5gxA4GBgdi2bRvMzc1x5MgRzJs3T10eEhKCWrVq5eayUKdOnTxv37lv3z74+PjAwsIi123r1KkDBwcH2NjY5CmGqKgolC5dOsvOBPqKjo5Wb09YFKWnp7/2teeHlJQU3Lt3D25ubq+1y01+vA7zMgYdOnTI07aUeVGiRAmUKFEiX/ssW7asznJjY2OdKxgbGxvDw8NDa7muMn3lNYZXmZiYwMXl/1Zxl/+/Xae+ypUrh2XLluldn4iKjhNRTzV2fvgvARAdl4wTUU/hW06/XXCIiApKrrepvHz5Mnbs2IFbt25BpVLBxcUFDRs2hL+/f67+GDKElJQUZGRkwNLSskD637FjB0xMTBAQEKCzXlEew0yxsbEIDQ3Ncg0VKlTIse3Zs2fx+++/IyUlBQEBAWjVqlWuzh0aGopDhw6hadOmaNGiBQ4cOIDZs2cjJSUFvXv3Rv/+/XN9Paampjh79iwqV66c67b50d6QMTx48AA//fSTervSgQMHamwB+1+PHj2Ck5OT+nlkZCTmz5+vbj9s2DA0a9ZMa/uQkBCUKlUK1apVg0qlwsyZM7FkyRLExMSo248dO1bn/4W8xrBy5UpUqlQJvr6+SE5ORnBwMFatWgURgVKpxIABA7BgwQKtiYbTp0+jWLFi8PT0BAD8+uuvWLJkiXq71mHDhqF79+4FNgbDhw/HBx98gMaNG2s9R07y2oeh27dv3x4ffPABunTp8lqJwfwYw7zGkJKSgokTJ+LEiRMIDAzE2LFj8eWXX2LOnDkAXiaNlixZovdWjo8ePcKjR4+ybItZvXr1XMdWkLhNJVHubI28j0/XR+ZYb0H3mgiqWargAyKid5K+79+5TjDklUqlynaPepVKhXv37uU4g4C0e/bsGbZv344+ffoYOpQCs2bNGvTv3x/Vq1fHtWvXsGjRIowaNQpdunSBSqXCmjVr8Ntvv6FLly7Ztq9du3a2xyMjI+Hl5QVzc3MALz9AZuf999/P9vjWrVvRokUL9SyGTZs2ab2GvPaR1/aWlpa4c+cOSpQogUuXLsHPzw8lSpRArVq1cP78edy9exdHjx7V+qHEyMgI0dHRcHJywpEjR9CsWTP4+fmhfv36iIyMRHh4OPbu3YsmTZpk297LywvLli1D48aNMXv2bHzzzTeYOHEiKleujKtXr2L27NkYNWqUztk0eY2hbNmyWLduHXx8fDBmzBhs3LgR3377rTqGzz//HEFBQZg7d2627WvUqIFvvvkG/v7++PnnnzFixAgMGjRI3f7nn3/GggUL8NFHHxXIGCiVSigUCpQrVw4DBgxA37594ezsrHW8CqKPwtDeyMgIVlZW6NGjBwYOHJirGST5NYZ5iWH06NHYsGEDevTogb///hvNmzfHjh07MGvWLCiVSkyZMgVt27bFwoULdfYTERGBvn374vLly/jvW7pCoUBGRkaurqugMcFAlDtHb/6LHsuO5Vhv3aAGnMFARAVG7/fvAr5VQy0uLk66du0q5ubm4uTkJJMnT5b09HR1eU67QKSmpsqYMWOkXLlyUq9ePVm+fLlGeX5sU5lXiYmJWXaGeJMiIyPfyBiqVCq5deuWpKWliYhISkqKrF+/XlatWqXesrKg2r+6XemePXvEwsJCvv32W3X5119/LQ0bNtTa3tjYWNq0aSPTpk1TP6ZOnSpKpVI++eQT9TFtFAqFNG3aNMsCoUqlUjp27Kh+rkte+8iP9pn36QcFBUn79u3VP4uMjAzp3r27vPfee3q1b9WqVZYdKz799FNp0aKF1vZmZmZy584dERGpWrWqxvaMIiI7duyQ8uXLa22f3zFUrFgxy6KT+/fv17nFooWFhfre+lq1amXZdeO3337TuZ5JXsdAoVDInj175NNPP5XixYuLiYmJdOjQQbZv36734pR57aMwtL948aLMnz9fqlWrJkqlUmrUqCGLFi3Sax2M/BrDvMRQunRp9UK5N2/eFKVSqbGF6+7du/XaprJ69erSqVMnOXbsmERFRcnt27c1HoUN12Agyp3MNRiyW+SRazAQ0ZuS74s85vXD6YgRI6RixYryxx9/yLJly8TDw0MCAwMlJSVF3V7XlmhTp06VkiVLyrx582TixIliZ2cngwcP1jh/TlvbFXSSoqA/4MfFxel8HDx4UGf7/BjDK1euiIeHhyiVSilfvrzcunVL6tSpI1ZWVmJpaSnFixfPsi98du0VCsVrtf/vdqUmJiZy9uxZ9fPLly+Lo6Oj1vaHDh2ScuXKyZQpUzQ+ROi7vd+6devEzc1NfvnlF43judkeMK995LX9qx/OS5curbFtqIjI6dOn9d4uNbvdXS5cuJDjVqWZbUqWLCmnT5/WKL927ZpYWFjofQ2vE4OHh4eEhYWJiEipUqXk5MmTGuWXLl0SKysrre0dHR3Vq/M7OTllu6OMrmvI6xi8ev2pqamyYcMGCQgIECMjI3F1dZUJEybI9evXtbbPjz4KU3sRkePHj8vgwYPFzs5OLCwspEePHrJ3794CO39+xGBhYaFONIm8/H124cIF9fOoqCi9dtWxtrbOMdbChAkGotzL3EXiv0kG7iJBRG9KvicY8vrh1N3dXcLDw9XPHz9+LPXr15fWrVtLcnJyjh+uy5cvL9u3b1c/v379upQvX1769esnKpVKr+RAfnzA1iWnBENez5+5C4S2R2a5NvkxhkFBQdKhQwc5d+6cjBw5UipXrixBQUGSmpoqycnJ0r59e+nVq1eBtbe3t5crV66on1tbW2tse3nr1q0c/yCPjY2V7t27i4+Pj3oXj9wkCKKioqRhw4by/vvvq7+lzE37/OgjL+2VSqV6u1UPDw+NBI3IyzE0NzfX2l6hUMiNGzckLi5OPD09s3w4vnHjhs6fwSeffCLvvfeepKeny+DBg2XgwIEaq/kPHz5cfH19dV5DXmOYMGGC+Pr6yrNnz2TcuHHSvn17SUhIEBGR58+fywcffKB1+0ERkV69esmAAQNERKRr164yadIkjfJZs2ZJtWrVtLbP6xho21Hmzp07MnXqVHUSUJe89lFY2z9//lxWrFghjRo1KtDz50cMlSpVUm/7e+LECTE1NdVIHK5fv14qVKigMwaRl79XN27cmGO9woIJBqLXs/P8gyy7STSYtYfJBSJ6I/I9wZDXD6cWFhYa3zyLiMTHx4uvr6+0aNFCbt26lWP7/25/du/ePalYsaL07NlT7t+/n+Mfg3m9hmLFiul82NraFugHfFtbW/nqq69k37592T6WLVtW4GNYokQJOXPmjIi8vCVEoVDIwYMH1eWHDx/WObU8r+3r1q2rMYU4Li5O44NZaGioVKxYUec1ZPrll1/E2dlZfvrpJzExMclVgiAjI0OmTJkipUuXlpCQkFy3z48+Xre9QqEQe3t7KVasmJiYmMivv/6qUb57924pU6aMzvavJrX+e3vA1q1bdU7vj42Nlbp160r58uWld+/eYm5uLh4eHtKqVSvx9PQUOzs7OXbsWI7XkJcYUlJSpEOHDlKsWDFp1aqVmJubi6WlpVSoUEGsrKzE3d1drl69qrX9/fv3pUyZMtKkSRMZPXq0WFhYSKNGjWTQoEHSpEkTMTU1lb/++qvAxiCnLWtVKpXs3r1ba3l+9FHY24uIzp/hmxjDnGKYP3++mJubi7+/vxQrVkwWLlwozs7O8vnnn8u4cePEzs5OZsyYobN/kZcJ+3bt2sm0adNk48aNsnXrVo1HYcMEA9HrS89QyZEbT2TLmXty5MYT3hZBRG9Mvm9Tef/+fVStWlX9vHz58ti3bx9atGiB3r17a10MLZO7uzsuX76sXnUdAGxsbLB79260bt0anTp10tne2dkZN2/eRJkyZdTHSpUqhfDwcDRv3hz9+vUr8GtISUnB0KFDUa1atWzL79y5g+nTpxfY+TMXKGzatGm25fb29lkW+HpVfoxhYmKieqtMKysrWFlZaWytVrp0aTx8+LDA2k+YMAHFihVTP//vAiOnTp3CBx98kON1AED//v3RqFEj9OzZE+np6Xq1yaRUKjF9+nS0atUKffr0ea1F1PLax+u2X7Fihcbz8uXLazw/duyYzv+P4eHhGs9f/fkBL7fbHDx4sNb2dnZ2OHLkCJYvX47t27ejTJkyUKlUSE1NRY8ePTB06FC4ubnpvIa8xmBqaoqtW7ciJCQE27dvh5GRkcZuKB9++CGsrKy0tnd1dcWZM2cwZ84cbN++HSKCEydO4J9//kHDhg1x+PBh1K1bt8DGwMPDA0ZGRlrLFQpFjruz5LUPQ7dv2rQpTE1NtZYDQMWKFQvs/PkRw8iRI+Hk5ISjR4/io48+Qo8ePVCtWjVMmTIFSUlJGDVqFCZOnKizfwA4evQoDh8+jJ07d2Z7HYVtkUcien1GSgUXciSiwk3fjIWnp6fs2bMny/H79+9LxYoVpVWrVjq//R4+fLh06dIl27L4+Hjx8fHR2X7AgAFZFnLLdO/ePSlfvnyO377n9Rr8/Pzku+++01qe0y0SeT3/0qVL1QscZicmJkbnAoX5MYblypXTmHHw448/Snx8vPp5RESEODs7F1j7gpCRkSGxsbEaMyFyIyEhQSIjI9XriRiij/yIgYiKJg8PDwkODpaYmBhDh6IXzmAgIiIqevR9/9Z7m8qBAwdCRLB8+fIsZffv30ezZs1w69Ytrd+UPHv2DA8ePECVKlWyLU9ISMDp06e1fjt/584dXLlyBQEBAdmWP3jwAKGhoejbt2+BXcOsWbOQlpaGqVOnZlv+zz//YMqUKVm+Ic6v8+dVfozhkCFDULduXQwcODDb8jlz5uDgwYP466+/CqQ9ERFpsrGxQWRkJMqVK2foUPTCbSqJiIiKHn3fv/VOMOTHh1NDM/Q1GPr8b0JUVBTMzc2zTFl/U+2JiN41ffv2RePGjbUmbgsbJhiIiIiKnnxPMBAREVHhM3PmTHz33XcIDAxEtWrVYGJiolE+YsQIA0WWPSYYiIiIih4mGIiIiN4Bry6e/F8KhQK3bt16g9HkjAkGIiKiokff92+9d5EgIiKiwicqKsrQIRAREREBYIKBiIjorZE5KVGhUBg4EiLKToZKcCLqKR4lJMPJxhz1PR1gpOT/VyJ6ezDBQEREVMStXr0a8+bNw/Xr1wEAFStWxJgxY9C7d28DR0ZEmUIuRGP69kuIjktWH3OxM8fU9t5oU5WLWxPR20Fp6ACIiIjo9X377bcYOnQo2rVrh99//x2///472rRpgyFDhmD+/PmGDo+I8DK5MHTNaY3kAgDExCVj6JrTCLkQbaDIiIjyFxd5JCIiKsI8PT0xffp09OnTR+P4qlWrMG3atEK3RgMXeaR3TYZK0OirsCzJhUwKAM525jg0tgVvlyCiQkvf92/OYCAiIirCoqOj4efnl+W4n58foqP5rSiRoZ2Ieqo1uQAAAiA6Lhknop6+uaCIiAoIEwxERERFWPny5fH7779nOb5hwwZUqFDBABER0aseJWhPLrxOPSKiwoyLPBIRERVh06dPR7du3XDgwAE0bNgQAHD48GHs3bs328QDEb1ZTjbm+VqPiKgw4wwGIiKiIqxz5844fvw4ihcvji1btmDLli0oXrw4Tpw4gU6dOhk6PKJ3Xn1PB7jYmUPb6goKvNxNor6nw5sMi4ioQHCRRyIiInpjuMgjvYsyd5EAXq65kCkz6bC4V21uVUlEhRoXeSQiInoH/P3339i1a1eW47t27cLOnTtfu985c+ZAoVBg5MiR6mPJyckIDg6Go6MjrK2t0blzZzx8+PC1z0H0rmhT1QWLe9WGs53mbRDOduZMLhDRW4VrMBARERVh48aNw5w5c7IcFxGMGzcObdu2zXWfJ0+exE8//YTq1atrHB81ahT++usv/PHHH7Czs8OwYcPw/vvv4/Dhw68dP5EhZKgEJ6Ke4lFCMpxsXt6eUNBbRLap6oJW3s5v/LxERG8SEwxERERF2PXr1+Ht7Z3luJeXF27cuJHr/hITE9GzZ08sW7YMX375pfp4XFwcli9fjrVr16JFixYAgBUrVqBy5co4duwYGjRo8PoXQfQGhVyIxvTtlzS2jnSxM8fU9t4FPpPASKmAbznHAj0HEZEh8RYJIiKiIszOzg63bt3KcvzGjRuwsrLKdX/BwcEIDAyEv7+/xvGIiAikpaVpHPfy8oK7uzuOHj2qtb+UlBTEx8drPIgMJXMthFeTCwAQE5eMoWtOI+RCtIEiIyJ6O3AGAxU5qamp2LJlC44ePYqYmBgAgLOzM/z8/BAUFARTU9PX7vvhw4f46aefMGXKFJ317t27B3t7e1hbW2scT0tLw9GjR9GkSROd7f/991+cO3cONWrUgIODA548eYLly5cjJSUFXbt2ReXKlXMde9myZbFr165c73svIti3bx9u3LgBFxcXBAQEwMTERGv9e/fuwdzcHMWLFwcAHDx4EEuWLMHdu3fh4eGB4OBg+Pr6am3/zTffoEuXLvDw8MhVnP+1Y8cOnDhxAgEBAWjYsCHCwsLw9ddfQ6VS4f3338fgwYN1tn/x4gXWrVuHQ4cOITo6GkqlEmXLlkXHjh3RsmVLvWI4ceJEltehr68v6tevn6dre/bsGbZv344+ffrorKdSqaBUZs0Tq1Qq3Lt3D+7u7jrbiwhu376N0qVLw9jYGKmpqdi8eTNSUlLQrl079c84N1q0aIEVK1a81s83KipK/TqsWrWqzropKSlQKpXq1+rNmzfxyy+/qF+HAwYMgKenp9b2f/75J9q2bQtLS8tcx/mqs2fPIiIiAs2aNUPZsmVx8eJF/PDDD1CpVOjUqRMCAgJy7CMsLCzL67BDhw56/18OCgrCyJEjsXnzZpQrVw7Ay+TCZ599hg4dOuTqetavX4/Tp0/j5MmTWcpiYmJgamoKe3t7jeMlS5ZU/x/IzuzZszF9+vRcxUFUEDJUgunbLyG71c0FLxdcnL79Elp5O/O2BSKi1yVERcj169elbNmyYm5uLk2bNpUPPvhAPvjgA2natKmYm5tL+fLl5fr166/df2RkpCiVSq3lDx48kHr16olSqRQjIyPp3bu3JCQkqMtjYmJ0thcROX78uNjZ2YlCoZBixYrJqVOnxNPTUypUqCDlypUTCwsLiYiI0Np+wYIF2T6MjIxk/Pjx6ufatG3bVmJjY0VE5N9//xUfHx9RKBRSokQJUSqV4uXlJY8ePdLavn79+rJ9+3YREdmyZYsolUrp0KGDjB07Vjp16iQmJibq8uwoFAoxMjISf39/Wb9+vaSkpOgcr+wsWbJEjI2NpU6dOmJrayu//vqr2NjYyMCBA+Xjjz8WCwsL+e6777S2v379unh4eIiTk5OULl1aFAqFBAYGio+PjxgZGUnXrl0lLS1Na/uHDx9Ko0aNRKFQiIeHh9SvX1/q168vHh4eolAopFGjRvLw4cNcX1emnF6HcXFx0rVrVzE3NxcnJyeZPHmypKenq8v1eR1euXJFPDw8RKlUSvny5eXWrVtSp04dsbKyEktLSylevLhcu3ZNa/utW7dm+zAyMpLvv/9e/VyboUOHqv/vJCUlSefOnUWpVIpCoRClUinNmzfX+L/1X02bNpU//vhDREQOHTokZmZmUr16denWrZvUqlVLLC0t5ciRI1rbKxQKsbW1lUGDBsmxY8d0jpU2f/75pxgZGYmjo6NYW1tLaGio2Nvbi7+/vwQEBIiRkZH89ttvWts/fPhQ6tevL0qlUoyNjUWpVEqdOnXE2dlZjIyMZMyYMXrFERsbKw0aNBBjY2MpU6aMlClTRoyNjaV58+by7Nkzva/n7t274uTkJGfPnlUfa9q0qXz66aciIvLbb7+Jqalplnb16tWTzz//XGu/ycnJEhcXp378888/AkDi4uL0jo0oPxy58UQ8xu7I8XHkxhNDh0pEVOjExcXp9f7NBAMVKf7+/hIUFJTtCzsuLk6CgoKkdevWWtufPXtW52PDhg06P5j16dNHfHx85OTJkxIaGip16tSRunXrytOnT0Xk5Qc7hUKR4zUMHDhQ4uPjZd68eeLm5iYDBw5Ul/fv3186duyotb1CoRA3Nzf1B4nMh0KhkFKlSkmZMmXE09NTZ/vMD79Dhw4Vb29vuXXrloiI/PPPP1KnTh0ZMmSI1vZWVlbq+j4+PjJnzhyN8kWLFkmtWrV0nn/FihUSFBQkJiYm4ujoKJ9++qmcP39ea5v/8vb2lqVLl4qISFhYmJibm8sPP/ygLl+xYoVUrlxZa/u2bdvKxx9/LCqVSkRE5syZI23bthURkWvXrkmZMmVk6tSpWtt37txZfH195cqVK1nKrly5In5+ftKlSxet7V/9sJXd4+DBgzpfhyNGjJCKFSvKH3/8IcuWLRMPDw8JDAxUJ2v0eR0GBQVJhw4d5Ny5czJy5EipXLmyBAUFSWpqqiQnJ0v79u2lV69eWttnJgIUCoXWh65rUCqV6tfh+PHjxc3NTcLCwuT58+dy6NAhKVeunIwbN05re1tbW3UCpGnTpjJq1CiN8kmTJknDhg11xj9jxgypVauWKBQKqVKlisyfP1+ePNH/g0Xt2rXlyy+/FBGRdevWib29vcyYMUNd/vXXX0vNmjW1tu/WrZt07NhR4uLiJDk5WYYNGyZ9+vQREZG9e/eKo6OjzkTZq1QqlezatUvmzp0rixYtkv379+t9HZk2b94sAMTIyEj9AKBOCu7Zs0cAZElauLu7y7fffqv3efT9A4Uov205c0+vBMOWM/cMHSoRUaHDBAO9lSwsLHR+ED137pxYWFhoLdf1oejVb0+1cXV1lePHj6ufZ34Qq1mzpvz77796fXNcrFgxuXTpkoiIpKamilKp1OgzIiJCSpUqpbX9xx9/LDVr1lT3kcnY2FguXryo89wimgmGSpUqZfmWec+ePToTFHZ2dupvOP/7baeIyI0bN8TS0lKv8z98+FC++uor8fLyEqVSKfXq1ZOlS5dKfHy8zmuwsLCQO3fuqJ+bmJhovC6ioqJ0xmBpaanx7XxKSoqYmJioP1xu2bJFypQpo7W9tbW1nD59Wmv5qVOnxNraWmt55utM2yOn16G7u7uEh4ernz9+/Fjq168vrVu3luTkZL1ehyVKlJAzZ86IiEhiYqIoFAo5ePCguvzw4cPi7u6utX2bNm0kMDAwy0yN13kdVq1aVdauXatRvnXrVqlYsaLW9lZWVnL58mURESlZsqRERkZqlN+4cSPHn0Hm+U+dOiVDhw4Ve3t7MTMzk65du8ru3btzvAYrKyuJiooSkZcf8E1MTOTcuXPq8ps3b+qMwdbWVi5cuKB+npiYKCYmJuo37l9//VUqVaqUYxz5JT4+Xs6fP6/xqFu3rvTq1UvOnz8vsbGxYmJiIhs3blS3uXLligCQo0eP6n0eJhjIUDiDgYjo9en7/s1FHqlIsbe3x+3bt7WW3759O8v9wa9ycHDAsmXLEBUVleVx69Yt7NixQ+f54+LiUKxYMfVzMzMzbNq0CWXKlEHz5s3x6NGjHK8hNTUVFhYWAAATExNYWlpq3OtevHhx/Pvvv1rbL1myBFOmTEFAQAC+//77HM+XHYXi5b2lz549U9+znal8+fJ48OCB1rZNmzbFunXrAAC1atXCvn37NMrDw8NRqlQpveJwcnLC559/jsuXL2Pfvn3w9vbGqFGj4OKiexVvR0dH3LlzBwDw4MEDpKen4+7du+ryO3fuwMHBQWt7e3t7JCQkqJ8nJSUhPT1dvX5H9erVER2tfaEvMzMznQvVJSQkwMzMTGu5jY0NZs+ejbCwsGwfS5cu1doWAB4/fqyxxkHx4sWxZ88eJCQkoF27dkhKStLZHni5U0DmGFlZWcHKykpj3EuXLo2HDx9qbb9z5060bNkSdevWzfH/jTaZr8OYmJgs2yHWqFED//zzj9a2Pj4+2L59OwCgXLlyOHv2rEZ5ZGSkztfAq+rUqYMff/wR0dHRWLZsGR4/fow2bdroXMMBePlzzPy/Ghsbi/T0dI3/u//++2+WdVpeZWZmph4DAFAqlcjIyEB6ejoAwM/PT+fvu1ft3bsXEyZMwMCBA/HRRx9pPPRlY2ODqlWrajysrKzg6OiIqlWrws7ODgMGDMDo0aMRHh6OiIgI9O/fH76+vtxBgoqE+p4OcLEzh7bVFRR4uZtEfU/9fncQEVFWXOSRipSBAweiT58+mDx5Mlq2bImSJUsCeLk44969e/Hll19i+PDhWtvXqVMHDx480LoAXWxsLESyW/7ppbJly+LcuXMai68ZGxvjjz/+QNeuXfHee+/leA2lS5fGrVu3UKZMGQAvF1V79YNddHR0jovrderUCfXr10efPn3w119/YcWKFTme91X9+vWDmZkZ0tLSEBUVhSpVqqjLYmJidCZp5syZg8aNG+PBgwdo1KgRJk6ciJMnT6Jy5cq4evUqNmzYgCVLlmht/+oHqlc1btwYjRs3xsKFC7Fhwwad8QcFBWHAgAHo27cvtm3bhj59+uCzzz6DUqmEQqHAmDFj0Lp1a63tW7VqhdGjR2PJkiUwMzPD+PHjUbNmTdjY2AAA7t69CycnJ63tu3Xrhr59+2L+/Plo2bIlbG1tAQDx8fHYu3cvRo8ejR49emhtX7t2bQAvkzXZsbe31/k6dHd3x+XLlzU+ANvY2GD37t1o3bo1OnXqpLVtJldXV9y9e1e9EOTcuXM1rvnx48caybTsjBo1Cs2bN0fPnj2xfft2zJ8/P8fzvmry5MmwtLSEUqnEgwcPNF6H//77r84dEL788ku0bdsWz58/R48ePfDZZ5/h+vXr6tfhwoULMX78eK3ts3sdmpubo3fv3ujduzdu3LiR4/8rf39/BAcHY/jw4diwYQNat26N8ePHY8WKFerXYaNGjbS2b9SoEaZMmYJVq1bB1NQUEyZMQNmyZdWJEX1+BgAwffp0zJgxA3Xr1oWLi4vW/2P5Yf78+VAqlejcuTNSUlIQEBCAH3/8scDOR5SfjJQKTG3vjaFrTkMBaCz2mPm/Zmp7by7wSESUF29kPoUe7t69K/3799dZJykpSQ4ePJjt9NsXL17IqlWrCio8nTLv4y5oERER6nvfRURWr14tfn5+4ubmJg0bNpR169bpbD9s2DA5cOBAQYdZ4ObMmSMuLi4a08wVCoW4uLjIV199pbPtpk2b5Ndff9Va/vTpU1m5cqXW8s8//1zrGg9paWnSoUOHHKemT5s2TefPasKECfL+++/r7COTSqWSWbNmqReF02dqer9+/TQeGzZs0CgfM2aMBAQE6Ozjxo0b0r17d7GxsVHfYmJiYiJ+fn6yefNmnW1fnZr+uhITE2XQoEFStWpVGTx4sKSkpMi8efPE1NRUFAqFNGvWTOc5Hj58KA0aNFC/hjw8PDRuefjjjz9k4cKFWtsnJyfLkCFDxNTUVJRKpZibm4u5ubkolUoxNTWVoUOHSnJystb2S5cu1bkQZ0xMjEybNk1r+fDhw7Wu8RAfHy8+Pj45vg4//vhjWbZsmdby2bNnS7t27XT2kSkpKUk+/vhjqVChgt6vw6ZNm0qzZs3Uj//G8sUXX0jTpk119nHkyBH1z/HVR6lSpXJcuyA/XocxMTHSqlUrsba2loCAAImNjZVhw4apX1cVKlSQGzduaG1/8+ZNKVeunBgbG4uJiYnY29tLaGiounzFihU616HI5OzsLKtXr87TtbxJvEWCDG3n+QfSYNYejdsiGszaIzvPPzB0aEREhZa+798KER1fk/3HkydP8Msvv2S7PWC/fv1QokSJ1050nD17FrVr10ZGRka25deuXUPr1q1x9+5dKBQKNGrUSOOb34cPH8LV1VVr+0yXL1/GsWPH4OvrCy8vL1y5cgULFixASkoKevXqhRYtWuQ6dlNTU5w9ezbXWws+f/4cv//+u3pbth49esDR0VFr/Ro1auCbb76Bv78/fv75Z4wYMQKDBg1Sf2P3888/Y8GCBVqnxGZ+u1uuXDn1t7/Ozs65ihkAvv/+e5w4cQLt2rVD9+7d8euvv2L27Nnq7QFnzJgBY2Ptk2Oio6OxePHibLcH7NevH4yMjPSKIyoqSuN1mNN05vyQnp6OpKQk9TfW2ZXfv38/T1swJiUlwcjISOcU+/+KiIjAoUOH0KdPH72+8dTl+fPnMDIygrm5eY51RQSPHj2CSqVC8eLFdW5v+SYkJycjLS1NPRMhJ9evX0dKSgq8vLx0vma1iY+PR0REhMbrsE6dOlpfH/nl2bNnWb7xf1VCQgJOnz6tdYaEPqKiomBubp7j7Sqv2rZtG8LDwzF+/HidM0D0cevWLZiamsLNzS3Huo8fP8atW7egUqng4uKinh2ky507d+Du7l4g3/bfunULSUlJer2ukpKScOjQIaSmpqJBgwavtTWoo6MjTpw4keV2p8IqPj4ednZ2iIuLK/D/K0TaZKgEJ6Ke4lFCMpxsXt4WwZkLRETa6fv+rXeC4eTJkwgICIClpSX8/f2zTE1PSkrCrl27ULdu3Wzbb9u2TWf/t27dwmeffaY1QdCpUyekpaVh5cqViI2NxciRI3Hp0iXs27cP7u7ueiUYQkJCEBQUBGtrayQlJWHz5s3o06cPatSoAZVKhf3792P37t1akwyjR4/O9viCBQvQq1cvdXLg22+/zbaet7c3Dh06BAcHB/zzzz9o0qQJnj17hooVK+LmzZswNjbGsWPHtH5QtrS0xOXLl+Hh4YHatWtj6NChGDRokLp87dq1mDlzJi5evJhte6VSidDQUGzfvh2//fYb4uLi0LZtWwwaNAjt2rWDUpnzkhxffvkl5s6di9atW+Pw4cMYOXIk5s2bh1GjRkGpVGL+/PkYOnSo1j3PT506BX9/f5QvXx4WFhY4evQoPvzwQ6SmpmLXrl3w9vZGSEiI3h8QiYjedWPHjoW1tTUmT55s6FD0wgQDERFR0aP3+7e+UyJ8fHxk8ODB2d4OoFKpZPDgwdKgQQPtUyXyuKWZk5OTxurcKpVKhgwZIu7u7nLz5k29Vk339fWViRMnisjLLcWKFSsmEyZMUJePGzdOWrVqpfMaatasqTGtt1mzZqJQKKRevXrSrFkzad68uc72mVNye/bsKX5+fhIbGysiIgkJCeLv7y89evTQ2t7R0VFOnTqlHo/sVk3PaQeFzPOnpqbKhg0b1Hu1u7q6yoQJE+T69eta24uIlCtXTv78808REYmMjBQjIyNZs2aNunzTpk1Svnx5re0bNmyoMfX7119/FR8fHxF5eXtCzZo1ZcSIETpj0EWfW20Kc/vCEENRb/+mYsjrLVuGbl8YYijq7QtLDCNGjBB7e3tp0qSJDBs2TEaNGqXxKGx4iwQREVHRk+/bVJqbm6u3BMvO5cuXxdzcXGu5q6urbNmyRWv5mTNndCYIbGxssmzLJyISHBwsbm5ucuDAgRwTDLa2tuoP0BkZGWJsbKxx3/X58+elZMmSWtvPnj1bPD09Ze/evRrHX2dbtrJly2bZBu3w4cNSunRpre179eolAwYMEBGRrl27yqRJkzTKZ82aJdWqVdPr/K+6c+eOTJ06VTw8PHIcw+y2B3x1m7Xbt2/r3B7QwsJCbt68qX6ekZEhJiYmEhMTIyIiu3fvFldXV50x6BIZGZnjNRTm9oUhhqLe/k3EcPXqVfHw8FAnRps0aSL3799Xl+eU8Myu/YMHD95Y+8IQQ1FvX1hiEJEsSe9XH7qS3obCBAMREVHRo+/7t943HTs7O+PEiRPw8vLKtvzEiRPq2yayU6dOHURERCAoKCjbcoVCoXPVdC8vL5w6dSrLOgeZ2/R16NAhp0tQnwd4ebuAubk57Ozs1GU2NjaIi4vT2nbcuHFo2bIlevXqhfbt22P27Nm5vuc88/zJyclZ7m0uVaoUHj9+rLXtV199hYYNG6Jp06aoW7cuvvnmG+zbt0+9BsOxY8ewefPmXMUDvFyRftq0aZg6dSr27Nmjs66zszMuXboEd3d3XL9+HRkZGbh06ZL6XvCLFy/qvPfayckJ0dHRKFu2LICXt9ikp6erp9lUqFABT58+1dpen1ttdDF0+8IQQ1FvXxhiGDt2LKpWrYpTp06pb9lq1KiR+patnGTXvmHDhm+sfWGIoai3LywxAC+3hiV6l3D9BCKiQkzfjMX3338vZmZmMmLECNm6dascO3ZMjh07Jlu3bpURI0aIhYWF/PDDD1rbHzhwQHbu3Km1PDExUfbt26e1fNasWdK2bVut5UOHDhWFQqHzGqpXr64Rw/nz5yUtLU0jRk9PT519iLy8naFPnz5SvXp1OX/+vJiYmOg9g6FatWpSq1Ytsba2lo0bN2qU79+/X0qVKqWzj2fPnsnYsWPF29tbzM3NxdTUVDw8POTDDz+UkydP6mxbpkwZefLkSY5x6jJp0iQpUaKEDBw4UDw9PWXcuHHi7u4uixcvliVLlkjp0qV1Tsn99NNPpWrVqrJz504JCwuT5s2bS7NmzdTlISEhUq5cOa3t83qrjaHbF4YYinr7whBDXm/ZMnT7whBDUW9fWGIoijiDgfKCO0AQERlGvt8iISKyfv168fHxEWNjY/Uf4cbGxuLj45Nlq7vCaPHixbJjxw6t5ePHj1ffgqCPdevWScmSJUWpVOqVYJg2bZrGIyQkRKP8f//7n3Tv3l3v8xtCRkaGzJw5U9577z2ZNWuWqFQqWbdunZQuXVocHR2lX79+kpiYqLV9QkKCfPDBB+rXkJ+fn8bWm7t27ZLff/9da/u83mpj6PaFIYai3r4wxJDXW7YM3b4wxFDU2xeGGDp16qTXo7BhgoFe187zD6TMK4mFzEeZ//9gkoGIqODk+y0SANCtWzd069YNaWlpePLkCQAUiq3p9DVkyBCd5bNmzcpVf927d0ejRo0QERGh17aEU6dO1Vk+b968XJ3fEJRKJSZMmKBxrHv37ujevbte7a2trbFhwwYkJycjPT0d1tbWGuWtW7fW2T6vt9oYun1hiKGoty8MMeT1li1Dty8MMRT19oUhhldv8SN622WoBNO3X0J2v5kFgALA9O2X0MrbmbdLEBEZUoGmOYjyWV5vtTF0+8IQQ1FvXxhiyOstW4ZuXxhiKOrtC0sMRRFnMNDrOHLjSZaZC9k9jtzI262gRESUPX3fvxUiOXxVSERERJRP9N5Hm+gVWyPv49P1kTnWW9C9JoJqlir4gIiI3jH6vn8r32BMRERERES55mRjnq/1iIioYDDBQERERESFWn1PB7jYmUPb6goKAC52L7esJCIiw2GCgYiIiIgKNSOlAlPbewNAliRD5vOp7b25wCMRkYExwUBEREREhV6bqi5Y3Ks2nO00b4NwtjPH4l610aaqi4EiIyKiTLnappKIiIiIyFDaVHVBK29nnIh6ikcJyXCyeXlbBGcuEBEVDkwwEBERFXFKpRKVK1fGxYsX1ccqV66Ma9euISMjw4CREeU/I6UCvuUcDR0GERFlgwkGIiKiIu6XX36Bvb29xrHZs2cjLi7OMAERERHRO0khImLoIIiIiOjdoO8+2kSFVYZKeIsGEb1z9H3/5gwGIiKit8CNGzdw8+ZNNGnSBBYWFhARKBT80EOUn0IuRGP69kuIjktWH3OxM8fU9t5cZJKICNxFgoiIqEj7999/0bJlS1SsWBHt2rVDdHQ0AGDAgAH47LPPDBwd0dsj5EI0hq45rZFcAICYuGQMXXMaIReiDRQZEVHhwQQDERFRETZq1CiYmJjg7t27sLS0VB/v1q0bQkJCDBgZ0dsjQyWYvv0SsruvOPPY9O2XkKHincdE9G7jLRJERERF2O7du7Fr1y64ublpHK9QoQLu3LljoKiI3i4nop5mmbnwKgEQHZeME1FPucMFEb3TOIOBiIioCHv+/LnGzIVMT58+hZmZmQEiInr7PErQnlx4nXpERG8rJhiIiIiKsMaNG2P16tXq5wqFAiqVCnPnzkXz5s0NGBnR28PJxjxf6xERva14iwQREVERNnfuXLRs2RKnTp1CamoqPv/8c1y8eBFPnz7F4cOHDR0e0VuhvqcDXOzMEROXnO06DAoAznYvt6wkInqXcQYDERFREVa1alVcu3YNjRo1QlBQEJ4/f473338fZ86cQbly5QwdHtFbwUipwNT23gBeJhNelfl8antvGCm5NSwRvdsUIsLlbomIiOiNiI+Ph52dHeLi4mBra2vocIhyJeRCNKZvv6Sx4KOLnTmmtvdGm6ouBoyMiKhg6fv+zVskiIiIirjk5GScO3cOjx49gkql0ijr0KGDgaIievu0qeqCVt7OOBH1FI8SkuFk8/K2CM5cICJ6iQkGIiKiIiwkJAR9+vTBkydPspQpFApkZGQYICqit5eRUsGtKImItOAaDEREREXY8OHD0bVrV0RHR0OlUmk8mFwgIiKiN4kJBiIioiLs4cOHGD16NEqWLGnoUIiIiOgdxwQDERFREdalSxfs27fP0GEQERERcRcJIiKioiwpKQldu3ZFiRIlUK1aNZiYmGiUjxgxwkCRZY+7SBARERU93EWCiIjoHbBu3Trs3r0b5ubm2LdvHxSK/1vNXqFQFLoEAxEREb29mGAgIiIqwiZOnIjp06dj3LhxUCp55yMREREZDv8SISIiKsJSU1PRrVs3JheIiIjI4PjXCBERURHWt29fbNiwwdBhEBEREfEWCSIioqIsIyMDc+fOxa5du1C9evUsizx+++23BoqMiIiI3jVMMBARERVh58+fR61atQAAFy5c0Ch7dcFHIiIiooLGBAMREVERFh4ebugQiIiIiABwDQYiIiIiIiIiygdMMBARERVxp06dwueff47u3bvj/fff13jkxuLFi1G9enXY2trC1tYWvr6+2Llzp7o8OTkZwcHBcHR0hLW1NTp37oyHDx/m9+UQERFREcUEAxERURG2fv16+Pn54fLly9i8eTPS0tJw8eJFhIWFwc7OLld9ubm5Yc6cOYiIiMCpU6fQokULBAUF4eLFiwCAUaNGYfv27fjjjz+wf/9+PHjwINdJDCIiInp7KUREDB0EERERvZ7q1avj448/RnBwMGxsbHD27Fl4enri448/houLC6ZPn56n/h0cHDBv3jx06dIFJUqUwNq1a9GlSxcAwJUrV1C5cmUcPXoUDRo0yLZ9SkoKUlJS1M/j4+NRunRpxMXFwdbWNk+xERER0ZsRHx8POzu7HN+/OYOBiIioCLt58yYCAwMBAKampnj+/DkUCgVGjRqFpUuXvna/GRkZWL9+PZ4/fw5fX19EREQgLS0N/v7+6jpeXl5wd3fH0aNHtfYze/Zs2NnZqR+lS5d+7ZiIiIiocGOCgYiIqAgrVqwYEhISAAClSpVSb1UZGxuLpKSkXPd3/vx5WFtbw8zMDEOGDMHmzZvh7e2NmJgYmJqawt7eXqN+yZIlERMTo7W/8ePHIy4uTv34559/ch0TERERFQ3cppKIiKgIa9KkCUJDQ1GtWjV07doVn376KcLCwhAaGoqWLVvmur9KlSohMjIScXFx2LhxI/r27Yv9+/e/dnxmZmYwMzN77fZERERUdDDBQEREVIR9//33SE5OBgBMnDgRJiYmOHLkCDp37oxJkybluj9TU1OUL18eAFCnTh2cPHkSCxYsQLdu3ZCamorY2FiNWQwPHz6Es7NzvlwLERERFW1MMBARERVhDg4O6n8rlUqMGzcuX/tXqVRISUlBnTp1YGJigr1796Jz584AgKtXr+Lu3bvw9fXN13MSERFR0cQEAxEREQF4uV5C27Zt4e7ujoSEBKxduxb79u3Drl27YGdnhwEDBmD06NFwcHCAra0thg8fDl9fX607SBAREdG7hQkGIiKiIkipVEKhUOiso1AokJ6ernefjx49Qp8+fRAdHQ07OztUr14du3btQqtWrQAA8+fPh1KpROfOnZGSkoKAgAD8+OOPeboOIiIiensoREQMHQQRERHlztatW7WWHT16FAsXLoRKpVKvz1BY6LuPNhERERUe+r5/cwYDERFRERQUFJTl2NWrVzFu3Dhs374dPXv2xIwZMwwQGREREb2rlIYOgIiIiPLmwYMHGDRoEKpVq4b09HRERkZi1apV8PDwMHRoRUKGSnD05r/YGnkfR2/+iwwVJ3cSERG9Ds5gICIiKqLi4uIwa9YsLFq0CDVr1sTevXvRuHFjQ4dVpIRciMb07ZcQHfd/t5K42JljantvtKnqYsDIiIiIih7OYCAiIiqC5s6di7Jly2LHjh1Yt24djhw5wuRCLoVciMbQNac1kgsAEBOXjKFrTiPkQrSBIiMiIiqauMgjERFREaRUKmFhYQF/f38YGRlprbdp06Y3GFXOCssijxkqQaOvwrIkFzIpADjbmePQ2BYwUurerYOIiOhtx0UeiYiI3mJ9+vTJcZvKd0WGSnAi6ikeJSTDycYc9T0dckwKnIh6qjW5AAACIDouGSeinsK3nGM+R0xERPR2YoKBiIioCFq5cqWhQygUXncNhUcJ+m3fqW+9oup1kjNERETaMMFARERERVLmGgr/vdczcw2Fxb1qa00yONmY63UOfesVRVzgkoiI8hsXeSQiIqIiJ0MlmL79UpbkAgD1senbL2ndcrK+pwNc7Myh7bt6BV5+2K7v6ZAP0RY+XOCSiIgKAhMMREREVOTkZg2F7BgpFZja3hsAsiQZMp9Pbe/9Vt4ukNfkDBERkTZMMBAREVGRkx9rKLSp6oLFvWrD2U7zNghnO3Odt1cUdXlNzhAREWnDNRiIiIioyMmvNRTaVHVBK2/nd2qhw8KwwCUXlyQiejsxwUBERERFTuYaCjFxydlO9Vfg5UwEfdZQMFIq3qmtKA29wCUXlyQienvxFgkiIiIqct7lNRTyypALXHJxSSKitxsTDERERFQkvatrKOSVoZIzXFySiOjtx1skiIiIqMh6F9dQyA+ZyZn/3qrgXIC3KuRmccl36ZYVIqK3CRMMREREVKS9a2so5Jc3nZwpDItLEhFRwWKCgYiIiOgd9SaTM4ZeXJKIiAoe12AgIiIiogJnyMUliYjozWCCgYiIiIgKHHf+ICJ6+zHBQERERERvBHf+ICJ6u3ENBiIiIiJ6Y7jzBxHR24sJBiIiIiJ6o7jzBxHR24m3SBARERERERFRnjHBQERERERERER5xgQDEREREREREeUZEwxERERERERElGdMMBARERERERFRnnEXCSIiIqI8yFAJt1wkIiICEwxEREREry3kQjSmb7+E6Lhk9TEXO3NMbe+NNlVdDBgZERHRm8dbJIiIiIheQ8iFaAxdc1ojuQAAMXHJGLrmNEIuRBsoMiIiIsNggoGIiIgolzJUgunbL0GyKcs8Nn37JWSosqtBRET0dmKCgYiIiCiXTkQ9zTJz4VUCIDouGSeinr65oIiIiAyMazAQERER5dKjBO3JhdzU4wKRRET0NmGCgYiIiCiXnGzM81yPC0QSEdHbhrdIEBEREeVSfU8HuNiZQ9tcAwVeJgvqezpkW84FIomI6G3EBAMRERFRLhkpFZja3hsAsiQZMp9Pbe+d7e0OXCCSiIjeVkwwEBEREb2GNlVdsLhXbTjbad4G4WxnjsW9amu9zYELRBIR0duKazAQERERvaY2VV3Qyts5Vws15tcCkURERIUNEwxEREREeWCkVMC3nKPe9fNjgUgiIqLCiLdIEBEREQBg9uzZqFevHmxsbODk5ISOHTvi6tWrGnWSk5MRHBwMR0dHWFtbo3Pnznj48KGBIi6a8rpAJBERUWHFBAMREREBAPbv34/g4GAcO3YMoaGhSEtLQ+vWrfH8+XN1nVGjRmH79u34448/sH//fjx48ADvv/++AaMuevKyQCQREVFhphARLlFMREREWTx+/BhOTk7Yv38/mjRpgri4OJQoUQJr165Fly5dAABXrlxB5cqVcfToUTRo0CDHPuPj42FnZ4e4uDjY2toW9CUUaiEXojF9+yWNBR9d7Mwxtb231gUiiYiIDEHf92+uwUBERETZiouLAwA4OLycqh8REYG0tDT4+/ur63h5ecHd3V1rgiElJQUpKSnq5/Hx8QUcddHxOgtEEhERFWZMMBAREVEWKpUKI0eORMOGDVG1alUAQExMDExNTWFvb69Rt2TJkoiJicm2n9mzZ2P69OkFHW6RldsFIomIiAozrsFAREREWQQHB+PChQtYv359nvoZP3484uLi1I9//vknnyIkIiKiwoYzGIiIiEjDsGHDsGPHDhw4cABubm7q487OzkhNTUVsbKzGLIaHDx/C2dk5277MzMxgZmZW0CETERFRIcAZDERERAQAEBEMGzYMmzdvRlhYGDw9PTXK69SpAxMTE+zdu1d97OrVq7h79y58fX3fdLhERERUyHAGAxEREQF4eVvE2rVrsXXrVtjY2KjXVbCzs4OFhQXs7OwwYMAAjB49Gg4ODrC1tcXw4cPh6+ur1w4SRERE9HbjNpVEREQEAFAost+9YMWKFejXrx8AIDk5GZ999hnWrVuHlJQUBAQE4Mcff9R6i8R/cZtKIiKiokff928mGIiIiOiNYYKBiIio6NH3/ZtrMBARERERERFRnnENBiIiIiLKtQyV4ETUUzxKSIaTjTnqezrASJn9bTZERPRuYIKBiIiIiHIl5EI0pm+/hOi4ZPUxFztzTG3vjTZVXQwYGRERGRJvkSAiIiIivYVciMbQNac1kgsAEBOXjKFrTiPkQrSBIiMiIkNjgoGIiIiI9JKhEkzffgnZrRCeeWz69kvIUHENcSKidxETDERERESklxNRT7PMXHiVAIiOS8aJqKdvLigiIio0mGAgIiIiIr08StCeXHidekRE9HZhgoGIiIiI9OJkY56v9YiI6O3CBAMRERER6aW+pwNc7MyhbTNKBV7uJlHf0+FNhkVERIUEEwxEREREpBcjpQJT23sDQJYkQ+bzqe29YaTUloIgIqK3GRMMRERERKS3NlVdsLhXbTjbad4G4WxnjsW9aqNNVRcDRUZERIZmbOgAiIiIiKhoaVPVBa28nXEi6ikeJSTDyeblbRGcuUBE9G5jgoGIiIiIcs1IqYBvOUdDh0FERIUIEwxERERERVCGSjiDgIiIChUmGIiIiIiKmJAL0Zi+/RKi45LVx1zszDG1vTfXQCAiIoPhIo9ERERERUjIhWgMXXNaI7kAADFxyRi65jRCLkQbKDIiInrXMcFAREREVERkqATTt1+CZFOWeWz69kvIUGVXg4iIqGAxwUBERERURJyIeppl5sKrBEB0XDJORD19c0ERERH9f0wwEBERERURjxK0Jxdepx4REVF+YoKBiIiIqIhwsjHP13pERET5iQkGIiIioiKivqcDXOzMoW0zSgVe7iZR39PhTYZFREQEgAkGIiIioiLDSKnA1PbeAJAlyZD5fGp7bxgptaUgiIiICg4TDERERERFSJuqLljcqzac7TRvg3C2M8fiXrXRpqqLgSIjIqJ3nbGhAyAiIiKi3GlT1QWtvJ1xIuopHiUkw8nm5W0RnLlARESGxAQDERERURFkpFTAt5yjocMgIiJS4y0SRERERERERJRnTDAQERERERERUZ4xwUBEREREREREecYEAxERERERERHlGRMMRERERERERJRnTDAQERERERERUZ4xwUBEREREREREecYEAxERERERERHlGRMMRERERERERJRnxoYOgIiIiN4dIgIAiI+PN3AkREREpK/M9+3M93FtmGAgIiKiNyYhIQEAULp0aQNHQkRERLmVkJAAOzs7reUKySkFQURERJRPVCoVHjx4ABsbGygUinzrNz4+HqVLl8Y///wDW1vbfOv3bcXx0h/HSn8cK/1xrPTHsdJfQY6ViCAhIQGurq5QKrWvtMAZDERERPTGKJVKuLm5FVj/tra2/AM0Fzhe+uNY6Y9jpT+Olf44VvorqLHSNXMhExd5JCIiIiIiIqI8Y4KBiIiIiIiIiPKMCQYiIiIq8szMzDB16lSYmZkZOpQigeOlP46V/jhW+uNY6Y9jpb/CMFZc5JGIiIiIiIiI8owzGIiIiIiIiIgoz5hgICIiIiIiIqI8Y4KBiIiIiIiIiPKMCQYiIiIiIiIiyjMmGIiIiKjIOHDgANq3bw9XV1coFAps2bJFo1xEMGXKFLi4uMDCwgL+/v64fv26YYI1MF1jlZaWhrFjx6JatWqwsrKCq6sr+vTpgwcPHhguYAPL6bX1qiFDhkChUOC77757Y/EVJvqM1eXLl9GhQwfY2dnBysoK9erVw927d998sAaW01glJiZi2LBhcHNzg4WFBby9vbFkyRLDBGtAs2fPRr169WBjYwMnJyd07NgRV69e1aiTnJyM4OBgODo6wtraGp07d8bDhw8NFLFh5TReT58+xfDhw1GpUiVYWFjA3d0dI0aMQFxcXIHHxgQDERERFRnPnz9HjRo18MMPP2RbPnfuXCxcuBBLlizB8ePHYWVlhYCAACQnJ7/hSA1P11glJSXh9OnTmDx5Mk6fPo1Nmzbh6tWr6NChgwEiLRxyem1l2rx5M44dOwZXV9c3FFnhk9NY3bx5E40aNYKXlxf27duHc+fOYfLkyTA3N3/DkRpeTmM1evRohISEYM2aNbh8+TJGjhyJYcOGYdu2bW84UsPav38/goODcezYMYSGhiItLQ2tW7fG8+fP1XVGjRqF7du3448//sD+/fvx4MEDvP/++waM2nByGq8HDx7gwYMH+Prrr3HhwgWsXLkSISEhGDBgQMEHJ0RERERFEADZvHmz+rlKpRJnZ2eZN2+e+lhsbKyYmZnJunXrDBBh4fHfscrOiRMnBIDcuXPnzQRViGkbr3v37kmpUqXkwoUL4uHhIfPnz3/jsRU22Y1Vt27dpFevXoYJqBDLbqyqVKkiM2bM0DhWu3ZtmThx4huMrPB59OiRAJD9+/eLyMvf5SYmJvLHH3+o61y+fFkAyNGjRw0VZqHx3/HKzu+//y6mpqaSlpZWoLFwBgMRERG9FaKiohATEwN/f3/1MTs7O/j4+ODo0aMGjKxoiIuLg0KhgL29vaFDKZRUKhV69+6NMWPGoEqVKoYOp9BSqVT466+/ULFiRQQEBMDJyQk+Pj46bzl5l/n5+WHbtm24f/8+RATh4eG4du0aWrdubejQDCpzKr+DgwMAICIiAmlpaRq/3728vODu7s7f78g6Xtrq2NrawtjYuEBjYYKBiIiI3goxMTEAgJIlS2ocL1mypLqMspecnIyxY8eiR48esLW1NXQ4hdJXX30FY2NjjBgxwtChFGqPHj1CYmIi5syZgzZt2mD37t3o1KkT3n//fezfv9/Q4RU6ixYtgre3N9zc3GBqaoo2bdrghx9+QJMmTQwdmsGoVCqMHDkSDRs2RNWqVQG8/P1uamqaJQHK3+/Zj9d/PXnyBF988QUGDx5c4PEUbPqCiIiIiAq1tLQ0fPDBBxARLF682NDhFEoRERFYsGABTp8+DYVCYehwCjWVSgUACAoKwqhRowAANWvWxJEjR7BkyRI0bdrUkOEVOosWLcKxY8ewbds2eHh44MCBAwgODoarq6vGt/XvkuDgYFy4cAGHDh0ydChFQk7jFR8fj8DAQHh7e2PatGkFHg9nMBAREdFbwdnZGQCyrCr+8OFDdRlpykwu3LlzB6GhoZy9oMXBgwfx6NEjuLu7w9jYGMbGxrhz5w4+++wzlClTxtDhFSrFixeHsbExvL29NY5Xrlz5ndxFQpcXL15gwoQJ+Pbbb9G+fXtUr14dw4YNQ7du3fD1118bOjyDGDZsGHbs2IHw8HC4ubmpjzs7OyM1NRWxsbEa9d/13+/axitTQkIC2rRpAxsbG2zevBkmJiYFHhMTDERERPRW8PT0hLOzM/bu3as+Fh8fj+PHj8PX19eAkRVOmcmF69evY8+ePXB0dDR0SIVW7969ce7cOURGRqofrq6uGDNmDHbt2mXo8AoVU1NT1KtXL8sWg9euXYOHh4eBoiqc0tLSkJaWBqVS8yOZkZGReibIu0JEMGzYMGzevBlhYWHw9PTUKK9Tpw5MTEw0fr9fvXoVd+/efSd/v+c0XsDL97/WrVvD1NQU27Zte2O7uPAWCSIiIioyEhMTcePGDfXzqKgoREZGwsHBAe7u7hg5ciS+/PJLVKhQAZ6enpg8eTJcXV3RsWNHwwVtILrGysXFBV26dMHp06exY8cOZGRkqO9jdnBwgKmpqaHCNpicXlv/TcCYmJjA2dkZlSpVetOhGlxOYzVmzBh069YNTZo0QfPmzRESEoLt27dj3759hgvaQHIaq6ZNm2LMmDGwsLCAh4cH9u/fj9WrV+Pbb781YNRvXnBwMNauXYutW7fCxsZG/fvIzs4OFhYWsLOzw4ABAzB69Gg4ODjA1tYWw4cPh6+vLxo0aGDg6N+8nMYrM7mQlJSENWvWID4+HvHx8QCAEiVKwMjIqOCCK9A9KoiIiIjyUXh4uADI8ujbt6+IvNyqcvLkyVKyZEkxMzOTli1bytWrVw0btIHoGquoqKhsywBIeHi4oUM3iJxeW//1Lm9Tqc9YLV++XMqXLy/m5uZSo0YN2bJli+ECNqCcxio6Olr69esnrq6uYm5uLpUqVZJvvvlGVCqVYQN/w7T9PlqxYoW6zosXL+STTz6RYsWKiaWlpXTq1Emio6MNF7QB5TRe2l53ACQqKqpAY1P8/wCJiIiIiIiIiF4b12AgIiIiIiIiojxjgoGIiIiIiIiI8owJBiIiIiIiIiLKMyYYiIiIiIiIiCjPmGAgIiIiIiIiojxjgoGIiIiIiIiI8owJBiIiIiIiIiLKMyYYiIiIiIiIiCjPmGAgIiIiIiIiojxjgoGIiIiIyIB++OEHlClTBubm5vDx8cGJEyfeyHlnzpwJPz8/WFpawt7ePts6d+/eRWBgICwtLeHk5IQxY8YgPT1dZ79Pnz5Fz549YWtrC3t7ewwYMACJiYkadc6dO4fGjRvD3NwcpUuXxty5c7P088cff8DLywvm5uaoVq0a/v77b41yEcGUKVPg4uICCwsL+Pv74/r16wUSCxHphwkGIiIiIiID2bBhA0aPHo2pU6fi9OnTqFGjBgICAvDo0aMCP3dqaiq6du2KoUOHZluekZGBwMBApKam4siRI1i1ahVWrlyJKVOm6Oy3Z8+euHjxIkJDQ7Fjxw4cOHAAgwcPVpfHx8ejdevW8PDwQEREBObNm4dp06Zh6dKl6jpHjhxBjx49MGDAAJw5cwYdO3ZEx44dceHCBXWduXPnYuHChViyZAmOHz8OKysrBAQEIDk5OV9jIaJcECIiIiIiMoj69etLcHCw+nlGRoa4urrK7Nmz31gMK1asEDs7uyzH//77b1EqlRITE6M+tnjxYrG1tZWUlJRs+7p06ZIAkJMnT6qP7dy5UxQKhdy/f19ERH788UcpVqyYRh9jx46VSpUqqZ9/8MEHEhgYqNG3j4+PfPzxxyIiolKpxNnZWebNm6cuj42NFTMzM1m3bl2+xkJE+uMMBiIiIiIiA0hNTUVERAT8/f3Vx5RKJfz9/XH06FEDRvbS0aNHUa1aNZQsWVJ9LCAgAPHx8bh48aLWNvb29qhbt676mL+/P5RKJY4fP66u06RJE5iammr0e/XqVTx79kxd59VxyayTOS5RUVGIiYnRqGNnZwcfHx91nfyKhYj0xwQDEREREZEBPHnyBBkZGRof4AGgZMmSiImJMVBU/ycmJibb2DLLtLVxcnLSOGZsbAwHBwd1G3361Vbn1fJX22mrkx+xEJH+mGAgIiIiIiIiojxjgoGIiIiIyACKFy8OIyMjPHz4UOP4w4cP4ezsbKCo/o+zs3O2sWWWaWvz3wUq09PT8fTpU3UbffrVVufV8lfbaauTH7EQkf6YYCAiIiIiMgBTU1PUqVMHe/fuVR9TqVTYu3cvfH19DRjZS76+vjh//rzGh/TQ0FDY2trC29tba5vY2FhERESoj4WFhUGlUsHHx0dd58CBA0hLS9Pot1KlSihWrJi6zqvjklknc1w8PT3h7OysUSc+Ph7Hjx9X18mvWIgoFwy9yiQRERER0btq/fr1YmZmJitXrpRLly7J4MGDxd7eXmPnhoJy584dOXPmjEyfPl2sra3lzJkzcubMGUlISBARkfT0dKlataq0bt1aIiMjJSQkREqUKCHjx49X93H8+HGpVKmS3Lt3T32sTZs2UqtWLTl+/LgcOnRIKlSoID169FCXx8bGSsmSJaV3795y4cIFWb9+vVhaWspPP/2krnP48GExNjaWr7/+Wi5fvixTp04VExMTOX/+vLrOnDlzxN7eXrZu3Srnzp2ToKAg8fT0lBcvXuRrLESkPyYYiIiIiIgMaNGiReLu7i6mpqZSv359OXbs2Bs5b9++fQVAlkd4eLi6zu3bt6Vt27ZiYWEhxYsXl88++0zS0tLU5eHh4QJAoqKi1Mf+/fdf6dGjh1hbW4utra30799fnbTIdPbsWWnUqJGYmZlJqVKlZM6cOVni+/3336VixYpiamoqVapUkb/++kujXKVSyeTJk6VkyZJiZmYmLVu2lKtXr2rUya9YiEg/ChERQ86gICIiIiIiIqKij2swEBEREREREVGeMcFARERERERERHnGBAMRERERERER5RkTDERERERERESUZ0wwEBEREREREVGeMcFARERERGQgBw4cQPv27eHq6gqFQoEtW7a8sXMvXboUzZo1g62tLRQKBWJjY7PUefr0KXr27AlbW1vY29tjwIABSExM1Kt/EUHbtm2zva67d+8iMDAQlpaWcHJywpgxY5Cenq4uj46OxocffoiKFStCqVRi5MiRWfrftGkT6tatC3t7e1hZWaFmzZr49ddfNepMmzYNXl5esLKyQrFixeDv74/jx49r1Ll27RqCgoJQvHhx2NraolGjRggPD9frGolIExMMREREREQG8vz5c9SoUQM//PDDGz93UlIS2rRpgwkTJmit07NnT1y8eBGhoaHYsWMHDhw4gMGDB+vV/3fffQeFQpHleEZGBgIDA5GamoojR45g1apVWLlyJaZMmaKuk5KSghIlSmDSpEmoUaNGtv07ODhg4sSJOHr0KM6dO4f+/fujf//+2LVrl7pOxYoV8f333+P8+fM4dOgQypQpg9atW+Px48fqOu+99x7S09MRFhaGiIgI1KhRA++99x5iYmL0uk4i+j8KERFDB0FERERE9K5TKBTYvHkzOnbs+EbPu2/fPjRv3hzPnj2Dvb29+vjly5fh7e2NkydPom7dugCAkJAQtGvXDvfu3YOrq6vWPiMjI/Hee+/h1KlTcHFx0biunTt34r333sODBw9QsmRJAMCSJUswduxYPH78GKamphp9NWvWDDVr1sR3332X47XUrl0bgYGB+OKLL7Itj4+Ph52dHfbs2YOWLVviyZMnKFGiBA4cOIDGjRsDABISEmBra4vQ0FD4+/vneE4i+j+cwUBERERERFkcPXoU9vb26uQCAPj7+0OpVGa5zeBVSUlJ+PDDD/HDDz/A2dk5236rVaumTi4AQEBAAOLj43Hx4sXXilVEsHfvXly9ehVNmjTJtk5qaiqWLl0KOzs79awIR0dHVKpUCatXr8bz58+Rnp6On376CU5OTqhTp85rxUL0LjM2dABERERERFT4xMTEwMnJSeOYsbExHBwcdN4+MGrUKPj5+SEoKEhrv68mFwCon+f2toS4uDiUKlUKKSkpMDIywo8//ohWrVpp1NmxYwe6d++OpKQkuLi4IDQ0FMWLFwfwctbInj170LFjR9jY2ECpVMLJyQkhISEoVqxYrmIhIiYYiIiIiIgon2zbtg1hYWE4c+bMGzmfjY0NIiMjkZiYiL1792L06NEoW7YsmjVrpq7TvHlzREZG4smTJ1i2bBk++OADHD9+HE5OThARBAcHw8nJCQcPHoSFhQV+/vlntG/fHidPnoSLi8sbuQ6itwVvkSAiIiIioiycnZ3x6NEjjWPp6el4+vRptrc+AEBYWBhu3rwJe3t7GBsbw9j45feZnTt3Vn/od3Z2xsOHDzXaZT7X1q82SqUS5cuXR82aNfHZZ5+hS5cumD17tkYdKysrlC9fHg0aNMDy5cthbGyM5cuXq+PdsWMH1q9fj4YNG6J27dr48ccfYWFhgVWrVuUqFiJigoGIiIiIiLLh6+uL2NhYREREqI+FhYVBpVLBx8cn2zbjxo3DuXPnEBkZqX4AwPz587FixQp1v+fPn9dIXoSGhsLW1hbe3t55ilmlUiElJUXvOklJSQBeJipepVQqoVKp8hQL0buIt0gQERERERlIYmIibty4oX4eFRWFyMhIODg4wN3dvUDPHRMTg5iYGPX5z58/DxsbG7i7u8PBwQGVK1dGmzZtMGjQICxZsgRpaWkYNmwYunfvrt5B4v79+2jZsiVWr16N+vXrw9nZOdtZCO7u7vD09AQAtG7dGt7e3ujduzfmzp2LmJgYTJo0CcHBwTAzM1O3yUxOJCYm4vHjx4iMjISpqak6CTF79mzUrVsX5cqVQ0pKCv7++2/8+uuvWLx4MYCXW4DOnDkTHTp0gIuLC548eYIffvgB9+/fR9euXQG8THYUK1YMffv2xZQpU2BhYYFly5YhKioKgYGBBTPwRG8xJhiIiIiIiAzk1KlTaN68ufr56NGjAQB9+/bFypUrC/TcS5YswfTp09XPM3dfWLFiBfr16wcA+O233zBs2DC0bNkSSqUSnTt3xsKFC9Vt0tLScPXqVfVMAH0YGRlhx44dGDp0KHx9fWFlZYW+fftixowZGvVq1aql/ndERATWrl0LDw8P3L59G8DLBMInn3yCe/fuwcLCAl5eXlizZg26deumPs+VK1ewatUqPHnyBI6OjqhXrx4OHjyIKlWqAACKFy+OkJAQTJw4ES1atEBaWhqqVKmCrVu3qneaICL9KUREDB0EERERERERERVtXIOBiIiIiIiIiPKMCQYiIiIiIiIiyjMmGIiIiIiIiIgoz5hgICIiIiIiIqI8Y4KBiIiIiIiIiPKMCQYiIiIiIgOZPXs26tWrBxsbGzg5OaFjx464evXqGzn3zJkz4efnB0tLS9jb22db5+7duwgMDISlpSWcnJwwZswYpKen6+z32rVrCAoKQvHixWFra4tGjRohPDw827r//vsv3NzcoFAoEBsbqz6+adMmtGrVCiVKlICtrS18fX2xa9cujbYZGRmYPHkyPD09YWFhgXLlyuGLL77Aq5vkiQimTJkCFxcXWFhYwN/fH9evX9fo5/Tp02jVqhXs7e3h6OiIwYMHIzExUec1ElH2mGAgIiIiIjKQ/fv3Izg4GMeOHUNoaCjS0tLQunVrPH/+vMDPnZqaiq5du2Lo0KHZlmdkZCAwMBCpqak4cuQIVq1ahZUrV2LKlCk6+33vvfeQnp6OsLAwREREoEaNGnjvvfcQExOTpe6AAQNQvXr1LMcPHDiAVq1a4e+//0ZERASaN2+O9u3b48yZM+o6X331FRYvXozvv/8ely9fxldffYW5c+di0aJF6jpz587FwoULsWTJEhw/fhxWVlYICAhAcnIyAODBgwfw9/dH+fLlcfz4cYSEhODixYvo16+fPkNIRP+hkFdTfEREREREZDCPHz+Gk5MT9u/fjyZNmryRc65cuRIjR47UmEEAADt37sR7772HBw8eoGTJkgCAJUuWYOzYsXj8+DFMTU2z9PXkyROUKFECBw4cQOPGjQEACQkJsLW1RWhoKPz9/dV1Fy9ejA0bNmDKlClo2bIlnj17pnUmBQBUqVIF3bp1Uyc43nvvPZQsWRLLly9X1+ncuTMsLCywZs0aiAhcXV3x2Wef4X//+x8AIC4uDiVLlsTKlSvRvXt3LF26FJMnT0Z0dDSUypffvZ4/fx7Vq1fH9evXUb58+dwPKNE7jDMYiIiIiIgKibi4OACAg4ODgSMBjh49imrVqqmTCwAQEBCA+Ph4XLx4Mds2jo6OqFSpElavXo3nz58jPT0dP/30E5ycnFCnTh11vUuXLmHGjBlYvXq1+oO9LiqVCgkJCRrj4ufnh7179+LatWsAgLNnz+LQoUNo27YtACAqKgoxMTEaSQ07Ozv4+Pjg6NGjAICUlBSYmppqxGBhYQEAOHToUI5xEZEmJhiIiIiIiAoBlUqFkSNHomHDhqhataqhw0FMTIxGcgGA+nl2tzsAgEKhwJ49e3DmzBnY2NjA3Nwc3377LUJCQlCsWDEALz/U9+jRA/PmzYO7u7tesXz99ddITEzEBx98oD42btw4dO/eHV5eXjAxMUGtWrUwcuRI9OzZUyPG7K4hs6xFixaIiYnBvHnzkJqaimfPnmHcuHEAgOjoaL1iI6L/wwQDEREREVEhEBwcjAsXLmD9+vWGDuW1iQiCg4Ph5OSEgwcP4sSJE+jYsSPat2+v/sA+fvx4VK5cGb169dKrz7Vr12L69On4/fff4eTkpD7++++/47fffsPatWtx+vRprFq1Cl9//TVWrVqld7xVqlTBqlWr8M0338DS0hLOzs7w9PREyZIl9ZpZQUSa+L+GiIiIiMjAhg0bhh07diA8PBxubm6GDgcA4OzsjIcPH2ocy3zu7OycbZuwsDDs2LED69evR8OGDVG7dm38+OOPsLCwUH/wDwsLwx9//AFjY2MYGxujZcuWAIDixYtj6tSpGv2tX78eAwcOxO+//65xqwMAjBkzRj2LoVq1aujduzdGjRqF2bNna8SY3TW8Gv+HH36ImJgY3L9/H//++y+mTZuGx48fo2zZsrkaLyJigoGIiIiIyGBEBMOGDcPmzZsRFhYGT09PQ4ek5uvri/Pnz+PRo0fqY6GhobC1tYW3t3e2bZKSkgAgy7f/SqUSKpUKAPDnn3/i7NmziIyMRGRkJH7++WcAwMGDBxEcHKxus27dOvTv3x/r1q1DYGBgtuf673mMjIzU5/H09ISzszP27t2rLo+Pj8fx48fh6+ubpb+SJUvC2toaGzZsgLm5OVq1aqV9cIgoW8aGDoCIiIiI6F0VHByMtWvXYuvWrbCxsVGvDWBnZ6debLCg3L17F0+fPsXdu3eRkZGByMhIAED58uVhbW2N1q1bw9vbG71798bcuXMRExODSZMmITg4GGZmZgCAEydOoE+fPti7dy9KlSoFX19fFCtWDH379sWUKVNgYWGBZcuWISoqSp0kKFeunEYcT548AQBUrlxZvYvE2rVr0bdvXyxYsAA+Pj7qcbGwsICdnR0AoH379pg5cybc3d1RpUoVnDlzBt9++y0++ugjAC/Xgxg5ciS+/PJLVKhQAZ6enpg8eTJcXV3RsWNH9fm///57+Pn5wdraGqGhoRgzZgzmzJmjc0cLItJCiIiIiIjIIABk+1ixYkWBn7tv377Znjs8PFxd5/bt29K2bVuxsLCQ4sWLy2effSZpaWnq8vDwcAEgUVFR6mMnT56U1q1bi4ODg9jY2EiDBg3k77//1hpHZh/Pnj1TH2vatGm2sfXt21ddJz4+Xj799FNxd3cXc3NzKVu2rEycOFFSUlLUdVQqlUyePFlKliwpZmZm0rJlS7l69arG+Xv37i0ODg5iamoq1atXl9WrV+d+MIlIREQUIiJvOqlBRERERERERG8XrsFARERERERERHnGBAMRERERERER5RkTDERERERERESUZ0wwEBEREREREVGeMcFARERERERERHnGBAMRERERkYEsXrwY1atXh62tLWxtbeHr64udO3e+kXPPnDkTfn5+sLS0hL29fbZ1RowYgTp16sDMzAw1a9bMsc+nT59i+PDhqFSpEiwsLODu7o4RI0YgLi4u2/r//vsv3NzcoFAoEBsbm22dw4cPw9jYOMv5Dxw4gPbt28PV1RUKhQJbtmzRGduQIUOgUCjw3XffqY/t27cPCoUi28fJkydzvF4i0sQEAxERERGRgbi5uWHOnDmIiIjAqVOn0KJFCwQFBeHixYsFfu7U1FR07doVQ4cO1Vnvo48+Qrdu3fTq88GDB3jw4AG+/vprXLhwAStXrkRISAgGDBiQbf0BAwagevXqWvuLjY1Fnz590LJlyyxlz58/R40aNfDDDz/kGNfmzZtx7NgxuLq6ahz38/NDdHS0xmPgwIHw9PRE3bp1c+yXiDQZGzoAIiIiIqJ3Vfv27TWez5w5E4sXL8axY8dQpUqVAj339OnTAQArV67UWmfhwoUAgMePH+PcuXM59lm1alX8+eef6uflypXDzJkz0atXL6Snp8PY+P8+fixevBixsbGYMmWK1lkbQ4YMwYcffggjI6MsMxTatm2Ltm3b5hjT/fv3MXz4cOzatQuBgYEaZaampnB2dlY/T0tLw9atWzF8+HAoFIoc+yYiTZzBQERERERUCGRkZGD9+vV4/vw5fH19DR1OvomLi4Otra1GcuHSpUuYMWMGVq9eDaUy+48kK1aswK1btzB16tTXPrdKpULv3r0xZswYvRI227Ztw7///ov+/fu/9jmJ3mWcwUBEREREZEDnz5+Hr68vkpOTYW1tjc2bN8Pb29vQYeWLJ0+e4IsvvsDgwYPVx1JSUtCjRw/MmzcP7u7uuHXrVpZ2169fx7hx43Dw4EGNxERuffXVVzA2NsaIESP0qr98+XIEBATAzc3ttc9J9C5jgoGIiIiIyIAqVaqEyMhIxMXFYePGjejbty/2799f5JMM8fHxCAwMhLe3N6ZNm6Y+Pn78eFSuXBm9evXKtl1GRgY+/PBDTJ8+HRUrVnzt80dERGDBggU4ffq0Xrc73Lt3D7t27cLvv//+2ucketfxFgkiIiIiIgMyNTVF+fLlUadOHcyePRs1atTAggULDB1WniQkJKBNmzawsbHB5s2bYWJioi4LCwvDpZwisgAAy45JREFUH3/8AWNjYxgbG6sXcCxevDimTp2KhIQEnDp1CsOGDVPXmTFjBs6ePQtjY2OEhYXpFcPBgwfx6NEjuLu7q/u5c+cOPvvsM5QpUyZL/RUrVsDR0REdOnTIlzEgehdxBgMRERERUSGiUqmQkpJi6DBeW3x8PAICAmBmZoZt27bB3Nxco/zPP//Eixcv1M9PnjyJjz76CAcPHkS5cuVga2uL8+fPa7T58ccfERYWho0bN8LT01OvOHr37g1/f3+NYwEBAejdu3eWNRZEBCtWrECfPn00kiFElDtMMBARERERGcj48ePRtm1buLu7IyEhAWvXrsW+ffuwa9euAj/33bt38fTpU9y9excZGRmIjIwEAJQvXx7W1tYAgBs3biAxMRExMTF48eKFuo63tzdMTU1x//59tGzZEqtXr0b9+vURHx+P1q1bIykpCWvWrEF8fDzi4+MBACVKlICRkRHKlSunEceTJ08AAJUrV4a9vT2Al7tRvMrJyQnm5uYaxxMTE3Hjxg3186ioKERGRsLBwQHu7u5wdHSEo6OjRj8mJiZwdnZGpUqVNI6HhYUhKioKAwcOfI2RJKJMTDAQERERERnIo0eP0KdPH0RHR8POzg7Vq1fHrl270KpVqwI/95QpU7Bq1Sr181q1agEAwsPD0axZMwDAwIEDsX///ix1oqKiUKZMGaSlpeHq1atISkoCAJw+fRrHjx8H8DJR8arMNvnl1KlTaN68ufr56NGjAQB9+/bVufVmdpYvXw4/Pz94eXnlW3xE7yKFiIihgyAiIiIiIiKioo2LPBIRERERERFRnjHBQERERERERER5xgQDEREREREREeUZEwxERERERERElGdMMBARERERERFRnjHBQERERERERER5xgQDEREREVEhMGfOHCgUCowcOfKNnG/mzJnw8/ODpaUl7O3ts60zYsQI1KlTB2ZmZqhZs6befR89ehQtWrSAlZUVbG1t0aRJE7x48SJLvZSUFNSsWRMKhQKRkZHq47dv34ZCocjyOHbsWLbnW79+PRQKBTp27KhxPDExEcOGDYObmxssLCzg7e2NJUuWaNRp1qxZlvMMGTJE72slov9jbOgAiIiIiIjedSdPnsRPP/2E6tWrv7FzpqamomvXrvD19cXy5cu11vvoo49w/PhxnDt3Tq9+jx49ijZt2mD8+PFYtGgRjI2NcfbsWSiVWb/b/Pzzz+Hq6oqzZ89m29eePXtQpUoV9XNHR8csdW7fvo3//e9/aNy4cZay0aNHIywsDGvWrEGZMmWwe/dufPLJJ3B1dUWHDh3U9QYNGoQZM2aon1taWup1rUSkiQkGIiIiIiIDSkxMRM+ePbFs2TJ8+eWXb+y806dPBwCsXLlSa52FCxcCAB4/fqx3gmHUqFEYMWIExo0bpz5WqVKlLPV27tyJ3bt3488//8TOnTuz7cvR0RHOzs5az5WRkYGePXti+vTpOHjwIGJjYzXKjxw5gr59+6JZs2YAgMGDB+Onn37CiRMnNBIMlpaWOs9DRPrhLRJERERERAYUHByMwMBA+Pv7GzqUPHv06BGOHz8OJycn+Pn5oWTJkmjatCkOHTqkUe/hw4cYNGgQfv31V52zBTp06AAnJyc0atQI27Zty1I+Y8YMODk5YcCAAdm29/Pzw7Zt23D//n2ICMLDw3Ht2jW0bt1ao95vv/2G4sWLo2rVqhg/fjySkpJe4+qJiDMYiIiIiIgMZP369Th9+jROnjxp6FDyxa1btwAA06ZNw9dff42aNWti9erVaNmyJS5cuIAKFSpARNCvXz8MGTIEdevWxe3bt7P0Y21tjW+++QYNGzaEUqnEn3/+iY4dO2LLli3qmQeHDh3C8uXLNdZu+K9FixZh8ODBcHNzg7GxMZRKJZYtW4YmTZqo63z44Yfw8PCAq6srzp07h7Fjx+Lq1avYtGlTvo4N0buACQYiIiIiIgP4559/8OmnnyI0NBTm5uaGDidfqFQqAMDHH3+M/v37AwBq1aqFvXv34pdffsHs2bOxaNEiJCQkYPz48Vr7KV68OEaPHq1+Xq9ePTx48ADz5s1Dhw4dkJCQgN69e2PZsmUoXry41n4WLVqEY8eOYdu2bfDw8MCBAwcQHBwMV1dX9YyRwYMHq+tXq1YNLi4uaNmyJW7evIly5crlaTyI3jVMMBARERERGUBERAQePXqE2rVrq49lZGTgwIED+P7775GSkgIjIyMDRph7Li4uAABvb2+N45UrV8bdu3cBAGFhYTh69CjMzMw06tStWxc9e/bEqlWrsu3bx8cHoaGhAICbN2/i9u3baN++vbo8M7lhbGyMq1evwtXVFRMmTMDmzZsRGBgIAKhevToiIyPx9ddfa70lxcfHBwBw48YNJhiIcokJBiIiIiIiA2jZsiXOnz+vcax///7w8vLC2LFji1xyAQDKlCkDV1dXXL16VeP4tWvX0LZtWwAvF458dTHLBw8eICAgABs2bFB/uM9OZGSkOoHh5eWVZewmTZqEhIQELFiwAKVLl0ZycjLS0tKy7F5hZGSkTkZoOw/wf8kSItIfEwxERERERAZgY2ODqlWrahyzsrKCo6NjluMF4e7du3j69Cnu3r2LjIwM9Qfr8uXLw9raGsDLb/ETExMRExODFy9eqOt4e3vD1NQU9+/fR8uWLbF69WrUr18fCoUCY8aMwdSpU1GjRg3UrFkTq1atwpUrV7Bx40YAgLu7u0YcmecqV64c3NzcAACrVq2CqakpatWqBQDYtGkTfvnlF/z8888AAHNz8yxjZG9vDwDq46ampmjatCnGjBkDCwsLeHh4YP/+/Vi9ejW+/fZbAC9nQqxduxbt2rWDo6Mjzp07h1GjRqFJkyZvdMtQorcFEwxERERERO+gKVOmaNyOkPlhPjw8XL2t48CBA7F///4sdaKiolCmTBmkpaXh6tWrGrsujBw5EsnJyRg1ahSePn2KGjVqIDQ0NNe3G3zxxRe4c+cOjI2N4eXlhQ0bNqBLly656mP9+vUYP348evbsiadPn8LDwwMzZ87EkCFDALxMQuzZswffffcdnj9/jtKlS6Nz586YNGlSrs5DRC8pREQMHQQRERERERERFW3KnKsQEREREREREenGBAMRERERERER5RkTDERERERERESUZ0wwEBEREREREVGeMcFARERERERERHnGBAMRERERkYFMmzYNCoVC4+Hl5fVGzj1z5kz4+fnB0tIS9vb2WcrPnj2LHj16oHTp0rCwsEDlypWxYMGCHPt9+vQpevbsCVtbW9jb22PAgAFITEzUqLNr1y40aNAANjY2KFGiBDp37ozbt29r1ElJScHEiRPh4eEBMzMzlClTBr/88ou6PC0tDTNmzEC5cuVgbm6OGjVqICQkRKOPnMb39u3bWcozH3/88Yceo0hErzI2dABERERERO+yKlWqYM+ePernxsZv5k/01NRUdO3aFb6+vli+fHmW8oiICDg5OWHNmjUoXbo0jhw5gsGDB8PIyAjDhg3T2m/Pnj0RHR2N0NBQpKWloX///hg8eDDWrl0LAIiKikJQUBBGjx6N3377DXFxcRg1ahTef/99nD59Wt3PBx98gIcPH2L58uUoX748oqOjoVKp1OWTJk3CmjVrsGzZMnh5eWHXrl3o1KkTjhw5glq1aqnr6Rrf0qVLIzo6WiP+pUuXYt68eWjbtm0uRpOIAEAhImLoIIiIiIiI3kXTpk3Dli1bEBkZabAYVq5ciZEjRyI2NjbHusHBwbh8+TLCwsKyLb98+TK8vb1x8uRJ1K1bFwAQEhKCdu3a4d69e3B1dcXGjRvRo0cPpKSkQKl8OaF6+/btCAoKQkpKCkxMTBASEoLu3bvj1q1bcHBwyPZcrq6umDhxIoKDg9XHOnfuDAsLC6xZswbA641vrVq1ULt27WyTLkSkG2+RICIiIiIyoOvXr8PV1RVly5ZFz549cffuXUOHpFVcXJzWD/wAcPToUdjb26uTCwDg7+8PpVKJ48ePAwDq1KkDpVKJFStWICMjA3Fxcfj111/h7+8PExMTAMC2bdtQt25dzJ07F6VKlULFihXxv//9Dy9evFD3m5KSAnNzc43zW1hY4NChQxrHcjO+ERERiIyMxIABA/QfFCJSY4KBiIiIiMhAfHx8sHLlSoSEhGDx4sWIiopC48aNkZCQYOjQsjhy5Ag2bNiAwYMHa60TExMDJycnjWPGxsZwcHBATEwMAMDT0xO7d+/GhAkTYGZmBnt7e9y7dw+///67us2tW7dw6NAhXLhwAZs3b8Z3332HjRs34pNPPlHXCQgIwLfffovr169DpVIhNDQUmzZt0rjlIbfju3z5clSuXBl+fn6vNUZE7zomGIiIiIiIDKRt27bo2rUrqlevjoCAAPz999+IjY3V+LBdGFy4cAFBQUGYOnUqWrdunae+YmJiMGjQIPTt2xcnT57E/v37YWpqii5duiDz7m2VSgWFQoHffvsN9evXR7t27fDtt99i1apV6lkMCxYsQIUKFeDl5QVTU1MMGzYM/fv3V992AeRufF+8eIG1a9dy9gJRHjDBQERERERUSNjb26NixYq4ceOGoUNRu3TpElq2bInBgwdj0qRJOus6Ozvj0aNHGsfS09Px9OlTODs7AwB++OEH2NnZYe7cuahVqxaaNGmCNWvWYO/everbKFxcXFCqVCnY2dmp+6lcuTJEBPfu3QMAlChRAlu2bMHz589x584dXLlyBdbW1ihbtqzW+HSN78aNG5GUlIQ+ffroNzBElAUTDEREREREhURiYiJu3rwJFxcXQ4cCALh48SKaN2+Ovn37YubMmTnW9/X1RWxsLCIiItTHwsLCoFKp4OPjAwBISkrSmGUAAEZGRgCg3iWiYcOGePDggcb2lteuXYNSqYSbm5tGW3Nzc5QqVQrp6en4888/ERQUpDU+XeO7fPlydOjQASVKlMjxOokoe0wwEBEREREZyP/+9z/s378ft2/fxpEjR9CpUycYGRmhR48eBX7uu3fvIjIyEnfv3kVGRgYiIyMRGRmp/lB/4cIFNG/eHK1bt8bo0aMRExODmJgYPH78WN3HiRMn4OXlhfv37wN4OcugTZs2GDRoEE6cOIHDhw9j2LBh6N69O1xdXQEAgYGBOHnyJGbMmIHr16/j9OnT6N+/Pzw8PNTbS3744YdwdHRE//79cenSJRw4cABjxozBRx99BAsLCwDA8ePHsWnTJty6dQsHDx5EmzZtoFKp8Pnnn+d6fG/cuIEDBw5g4MCBBTfgRO8AJhiIiIiIiAzk3r176NGjBypVqoQPPvgAjo6OOHbs2Bv5Fn3KlCmoVasWpk6disTERNSqVQu1atXCqVOnALy8ZeDx48dYs2YNXFxc1I969eqp+0hKSsLVq1eRlpamPvbbb7/By8sLLVu2RLt27dCoUSMsXbpUXd6iRQusXbsWW7ZsQa1atdCmTRuYmZkhJCREnTywtrZGaGgoYmNjUbduXfTs2RPt27fHwoUL1f0kJydj0qRJ8Pb2RqdOnVCqVCkcOnQI9vb26jr6ju8vv/wCNze3PK8vQfSuU0jmSipERERERERERK+JMxiIiIiIiIiIKM+YYCAiIiIiIiKiPGOCgYiIiIiIiIjyjAkGIiIiIiIiIsozJhiIiIiIiIiIKM+YYCAiIiIiMqD79++jV69ecHR0hIWFBapVq6beKrIgLV26FM2aNYOtrS0UCgViY2M1ym/fvo0BAwbA09MTFhYWKFeuHKZOnYrU1FS9+hcRtG3bFgqFAlu2bNEoO3nyJFq2bAl7e3sUK1YMAQEBOHv2rEadXbt2oUGDBrCxsUGJEiXQuXNn3L59W12+adMmtGrVCiVKlICtrS18fX2xa9cujT4SEhIwcuRIeHh4wMLCAn5+fjh58qTWmIcMGQKFQoHvvvtOr2skIk1MMBARERERGcizZ8/QsGFDmJiYYOfOnbh06RK++eYbFCtWrMDPnZSUhDZt2mDChAnZll+5cgUqlQo//fQTLl68iPnz52PJkiVa6//Xd999B4VCkeV4YmIi2rRpA3d3dxw/fhyHDh2CjY0NAgICkJaWBgCIiopCUFAQWrRogcjISOzatQtPnjzB+++/r+7nwIEDaNWqFf7++29ERESgefPmaN++Pc6cOaOuM3DgQISGhuLXX3/F+fPn0bp1a/j7++P+/ftZ4tq8eTOOHTsGV1dXva6PiLJSiIgYOggiIiIionfRuHHjcPjwYRw8eNBgMezbtw/NmzfHs2fPYG9vr7PuvHnzsHjxYty6dUtnvcjISLz33ns4deoUXFxcsHnzZnTs2BEAcOrUKdSrVw93795F6dKlAQDnz59H9erVcf36dZQvXx4bN25Ejx49kJKSAqXy5Xei27dvR1BQEFJSUmBiYpLteatUqYJu3bphypQpePHiBWxsbLB161YEBgaq69SpUwdt27bFl19+qT52//59+Pj4YNeuXQgMDMTIkSMxcuTIHEaOiP6LMxiIiIiIiAxk27ZtqFu3Lrp27QonJyfUqlULy5YtM3RYWsXFxcHBwUFnnaSkJHz44Yf44Ycf4OzsnKW8UqVKcHR0xPLly5GamooXL15g+fLlqFy5MsqUKQPgZRJAqVRixYoVyMjIQFxcHH799Vf4+/trTS6oVCokJCSo40tPT0dGRgbMzc016llYWODQoUMa7Xr37o0xY8agSpUquRkOIvoPJhiIiIiIiAzk1q1bWLx4MSpUqIBdu3Zh6NChGDFiBFatWmXo0LK4ceMGFi1ahI8//lhnvVGjRsHPzw//j707j8qq3P///7yRUQQUBBkUcEhxxNnQHEicMpUy5xTHjh1M0fKopdnobGlmmkXOZGrOmUMOqIUTyskRRVGTQa2cAAWE/fvDn/fn3AdQC5O+x9djrb0W93W993W993a1Vvf7vva1O3XqlG+/k5MTO3fuZMmSJTg4OFCiRAk2bdrE999/j7W1NQDly5dny5YtvPnmm9jZ2VGyZEkuXrzI8uXLC5x32rRppKWl0bVrV/M8QUFBvP/++yQnJ5OTk8OSJUuIiYkhJSXFfN7kyZOxtrZm6NChf/SWiMh/UYFBRERERKSI5ObmUrduXSZMmECdOnV45ZVXGDRoEHPnzi3q1CwkJSXRtm1bunTpwqBBgwqMW7duHdu3b7/vJom3bt1iwIABNGnShL179/Ljjz9So0YN2rdvz61btwBITU1l0KBBhIWFceDAAaKjo7G1teWll14ivye8o6KiePfdd1m+fDkeHh7m9sWLF2MYBj4+PtjZ2fHJJ5/Qo0cP82MXsbGxzJw5kwULFuS7X4SI/DEqMIiIiIiIFBEvLy+qVatm0Va1alUuXLhQRBnllZycTHBwMI0bN2bevHn3jd2+fTtnzpyhZMmSWFtbm1ckdO7cmRYtWgB3iwHnzp1j/vz5NGjQgKeffpqoqCgSExNZu3YtALNnz8bFxYUpU6ZQp04dmjVrxpIlS9i2bRv79u2zmHPZsmUMHDiQ5cuXExISYtFXsWJFoqOjSUtL45dffmH//v1kZ2dToUIFAHbv3s3ly5fx9fU153v+/Hlef/118+MaIvLwrIs6ARERERGRJ1WTJk2Ij4+3aDt16hR+fn5FlJGlpKQkgoODqVevHvPnzzf/8l+Q0aNHM3DgQIu2mjVr8vHHH9OhQwfg7h4NVlZWFisG7n3Ozc21iPlPxYoVAzDHAHz99df079+fZcuWWWzk+N8cHR1xdHTk6tWrbN68mSlTpgDQu3fvPEWJNm3a0Lt3b/r163ffaxWRvFRgEBEREREpIvf2K5gwYQJdu3Zl//79zJs374ErBR6F1NRUUlNTSUhIAO6+ycHJyQlfX19cXV1JSkqiRYsW+Pn5MW3aNK5cuWI+997mjUlJSbRs2ZJFixbRsGFDPD09893Y0dfXl/LlywPQqlUrRo4cSXh4OK+99hq5ublMmjQJa2trgoODAWjfvj0ff/wx7733Hj169ODmzZu8+eab+Pn5UadOHeDuSoiwsDBmzpxJo0aNSE1NBe5u4uji4gLA5s2bMQyDKlWqkJCQwMiRIwkICDAXD9zc3HBzc7PI1cbGBk9PT6pUqfLI7rXIk0KPSIiIiIiIFJEGDRqwevVqvv76a2rUqMH777/PjBkz6NWr118+99y5c6lTp455T4VmzZpRp04d1q1bB8DWrVtJSEhg27ZtlC1bFi8vL/NxT3Z2NvHx8WRkZDz0vAEBAaxfv56ff/6ZoKAgmjZtSnJyMps2bTKP/eyzzxIVFcWaNWuoU6cObdu2xc7Ojk2bNuHg4ADAvHnzuHPnDuHh4Ra5DRs2zDzX9evXCQ8PJyAggD59+vDMM8+wefPmAt9EISKFYzLy2yVFREREREREROQP0AoGERERERERESk0FRhEREREREREpNBUYBARERERERGRQlOBQUREREREREQKTQUGERERERERESk0FRhERERERIqIv78/JpMpzxEeHv6Xz/3hhx/SuHFjihcvTsmSJfP0//vf/6ZHjx6UK1cOBwcHqlatysyZMx847qlTp+jUqROlS5fG2dmZZ555hh07dljEHDhwgJYtW1KyZElKlSpFmzZt+Pe//20Rs3z5cmrXrk3x4sXx8/Nj6tSpeebKzMzkrbfews/PDzs7O/z9/fnqq6/M/atWraJ+/fqULFkSR0dHateuzeLFi8392dnZjBo1ipo1a+Lo6Ii3tzd9+vQhOTn5gdcpInmpwCAiIiIiUkQOHDhASkqK+di6dSsAXbp0+cvnzsrKokuXLrz66qv59sfGxuLh4cGSJUs4duwYb731FmPGjOHTTz+977jPP/88d+7cYfv27cTGxhIYGMjzzz9PamoqAGlpabRt2xZfX1/27dvHnj17cHJyok2bNmRnZwPw/fff06tXLwYPHszRo0f57LPP+Pjjj/PM3bVrV7Zt20ZkZCTx8fF8/fXXVKlSxdzv6urKW2+9RUxMDD///DP9+vWjX79+bN68GYCMjAwOHTrEuHHjOHToEKtWrSI+Pp6OHTv+6fsq8iQzGYZhFHUSIiIiIiICERERbNiwgdOnT2MymR7LnAsWLCAiIoJr1649MDY8PJwTJ06wffv2fPt//fVX3N3d2bVrF02bNgXg5s2bODs7s3XrVkJCQjh48CANGjTgwoULlCtXDoAjR45Qq1YtTp8+TaVKlejZsyfZ2dmsWLHCPPasWbOYMmUKFy5cwGQysWnTJrp3787Zs2dxdXV96OutW7cu7du35/3338+3/8CBAzRs2JDz58/j6+v70OOKiFYwiIiIiIj8LWRlZbFkyRL69+//2IoLf9T169fv+2Xezc2NKlWqsGjRItLT07lz5w6ff/45Hh4e1KtXD4AqVarg5uZGZGQkWVlZ3Lp1i8jISKpWrYq/vz9w99EHe3t7i7EdHBy4ePEi58+fB2DdunXUr1+fKVOm4OPjQ+XKlXnjjTe4detWvrkZhsG2bduIj4+nWbNm971Gk8mU72MjInJ/1kWdgIiIiIiIwJo1a7h27Rp9+/Yt6lTy9dNPP/HNN9/w3XffFRhjMpn44YcfCA0NxcnJCSsrKzw8PNi0aROlSpUCwMnJiZ07dxIaGmpeRfDUU0+xefNmrK3vfj1p06YNw4cPp2/fvgQHB5OQkMD06dMBSElJwd/fn7Nnz7Jnzx7s7e1ZvXo1v/76K//85z/57bffmD9/vjmn69ev4+PjQ2ZmJsWKFeOzzz6jVatW+eZ/+/ZtRo0aRY8ePXB2dn4k903kSaIVDCIiIiIifwORkZG0a9cOb2/vok4lj6NHj9KpUyfGjx9P69atC4wzDIPw8HA8PDzYvXs3+/fvJzQ0lA4dOpCSkgLArVu3GDBgAE2aNGHv3r38+OOP1KhRg/bt25tXHwwaNIghQ4bw/PPPY2try9NPP0337t0BsLK6+xUmNzcXk8nE0qVLadiwIc899xwfffQRCxcutFjF4OTkRFxcHAcOHODDDz9kxIgR7Ny5M0/u2dnZdO3aFcMwmDNnzqO6dSJPFK1gEBEREREpYufPn+eHH35g1apVRZ1KHsePH6dly5a88sorjB079r6x27dvZ8OGDVy9etW8AuCzzz5j69atLFy4kNGjRxMVFcW5c+eIiYkxFwuioqIoVaoUa9eupXv37phMJiZPnsyECRNITU3F3d2dbdu2AVChQgUAvLy88PHxwcXFxTx/1apVMQyDixcv8tRTTwF3CxKVKlUCoHbt2pw4cYKJEyfSokUL83n3igvnz59n+/btWr0g8idpBYOIiIiISBGbP38+Hh4etG/fvqhTsXDs2DGCg4MJCwvjww8/fGB8RkYG8H+rDO6xsrIiNzfXHGNlZWWxz8S9z/di7ilWrBg+Pj7Y2try9ddfExQUhLu7OwBNmjQhOTmZtLQ0c/ypU6ewsrKibNmyBeaYm5tLZmam+fO94sLp06f54YcfcHNze+B1ikj+VGAQERERESlCubm5zJ8/n7CwMPMeBI/DhQsXiIuL48KFC+Tk5BAXF0dcXJz5C/vRo0cJDg6mdevWjBgxgtTUVFJTU7ly5Yp5jP379xMQEEBSUhIAQUFBlCpVirCwMP79739z6tQpRo4cSWJiorl40qpVK65evWp+I8WxY8fo168f1tbWBAcHA3ffRjF37lxOnjxJXFwcw4YNY8WKFcyYMcM8d8+ePXFzc6Nfv34cP36cXbt2MXLkSPr374+DgwMAEydOZOvWrZw9e5YTJ04wffp0Fi9ezMsvvwzcLS689NJLHDx4kKVLl5KTk2O+zqysrL/830Dkf44hIiIiIiJFZvPmzQZgxMfHP9Z5w8LCDCDPsWPHDsMwDGP8+PH59vv5+ZnH2LFjhwEYiYmJ5rYDBw4YrVu3NlxdXQ0nJyfj6aefNjZu3Ggx95YtW4wmTZoYLi4uRqlSpYxnn33WiImJMfdfuXLFePrppw1HR0ejePHiRsuWLY29e/fmuYYTJ04YISEhhoODg1G2bFljxIgRRkZGhrn/rbfeMipVqmTY29sbpUqVMoKCgoxly5aZ+xMTE/O9xv+8DyLy8EyGYRiPu6ghIiIiIiIiIv9b9IiEiIiIiIiIiBSaCgwiIiIiIiIiUmgqMIiIiIiIiIhIoanAICIiIiIiIiKFpgKDiIiIiIiIiBSaCgwiIiIiIkUkJyeHcePGUb58eRwcHKhYsSLvv/8+j+NFb/PmzaNFixY4OztjMpm4du1anpiOHTvi6+uLvb09Xl5e9O7dm+Tk5ALH/P3333nttdeoUqUKDg4O+Pr6MnToUK5fv24Rt23bNho3boyTkxOenp6MGjWKO3fuWMT8/PPPNG3aFHt7e8qVK8eUKVMs+rOzs3nvvfeoWLEi9vb2BAYGsmnTJosYf39/TCZTniM8PNwcc/v2bcLDw3Fzc6NEiRJ07tyZS5cuPextFJH/oAKDiIiIiEgRmTx5MnPmzOHTTz/lxIkTTJ48mSlTpjBr1qy/fO6MjAzatm3Lm2++WWBMcHAwy5cvJz4+nm+//ZYzZ87w0ksvFRifnJxMcnIy06ZN4+jRoyxYsIBNmzYxYMAAc8y///1vnnvuOdq2bcvhw4f55ptvWLduHaNHjzbH3Lhxg9atW+Pn50dsbCxTp07lnXfeYd68eeaYsWPH8vnnnzNr1iyOHz/O4MGDeeGFFzh8+LA55sCBA6SkpJiPrVu3AtClSxdzzPDhw1m/fj0rVqwgOjqa5ORkXnzxxT92M0UEAJPxOMqjIiIiIiKSx/PPP0+ZMmWIjIw0t3Xu3BkHBweWLFnyWHLYuXMnwcHBXL16lZIlS943dt26dYSGhpKZmYmNjc1Djb9ixQpefvll0tPTsba25s0332Tr1q0cOHDAHLN+/Xq6du3K5cuXcXJyYs6cObz11lukpqZia2sLwOjRo1mzZg0nT54EwNvbm7feestiNcKD7l1ERAQbNmzg9OnTmEwmrl+/jru7O1FRUebCycmTJ6latSoxMTE8/fTTD3WNInKXVjCIiIiIiBSRxo0bs23bNk6dOgXc/XV/z549tGvXrogzy+v3339n6dKlNG7c+KGLCwDXr1/H2dkZa2trADIzM7G3t7eIcXBw4Pbt28TGxgIQExNDs2bNzMUFgDZt2hAfH8/Vq1fvO86ePXvyzSMrK4slS5bQv39/TCYTALGxsWRnZxMSEmKOCwgIwNfXl5iYmIe+RhG5SwUGEREREZEiMnr0aLp3705AQAA2NjbUqVOHiIgIevXqVdSpmY0aNQpHR0fc3Ny4cOECa9eufehzf/31V95//31eeeUVc1ubNm346aef+Prrr8nJySEpKYn33nsPgJSUFABSU1MpU6aMxVj3PqempprH+eijjzh9+jS5ubls3bqVVatWmcf4b2vWrOHatWv07dvX3HZvhcR/r9woU6aMeR4ReXgqMIiIiIiIFJHly5ezdOlSoqKiOHToEAsXLmTatGksXLiwqFMzGzlyJIcPH2bLli0UK1aMPn36PNQmlDdu3KB9+/ZUq1aNd955x9zeunVrpk6dyuDBg7Gzs6Ny5co899xzAFhZPfzXk5kzZ/LUU08REBCAra0tQ4YMoV+/fgWOERkZSbt27fD29n7oOUTkj1GBQURERESkiIwcOdK8iqFmzZr07t2b4cOHM3HixKJOzax06dJUrlyZVq1asWzZMjZu3MjevXvve87Nmzdp27YtTk5OrF69Os8jFSNGjODatWtcuHCBX3/9lU6dOgFQoUIFADw9PfO8yeHeZ09PTwDc3d1Zs2YN6enpnD9/npMnT1KiRAnzGP/p/Pnz/PDDDwwcONCi3dPTk6ysrDxv0Lh06ZJ5HhF5eCowiIiIiIgUkYyMjDy/uBcrVozc3Nwiyuj+7uWVmZlZYMy9N0DY2tqybt26PPsk3GMymfD29sbBwYGvv/6acuXKUbduXQCCgoLYtWsX2dnZ5vitW7dSpUoVSpUqZTGOvb09Pj4+3Llzh2+//dZcrPhP8+fPx8PDg/bt21u016tXDxsbG7Zt22Zui4+P58KFCwQFBT3gbojIf7Mu6gRERERERJ5UHTp04MMPP8TX15fq1atz+PBhPvroI/r37/+Xz52amkpqaioJCQkAHDlyBCcnJ3x9fXF1dWXfvn0cOHCAZ555hlKlSnHmzBnGjRtHxYoVzV++k5KSaNmyJYsWLaJhw4bm4kJGRgZLlizhxo0b3LhxA7i74qBYsWIATJ06lbZt22JlZcWqVauYNGkSy5cvN/f37NmTd999lwEDBjBq1CiOHj3KzJkz+fjjj83579u3j6SkJGrXrk1SUhLvvPMOubm5/Otf/7K4ztzcXObPn09YWJh5o8l7XFxcGDBgACNGjMDV1RVnZ2dee+01goKC9AYJkT/DEBERERGRInHjxg1j2LBhhq+vr2Fvb29UqFDBeOutt4zMzMy/fO7x48cbQJ5j/vz5hmEYxs8//2wEBwcbrq6uhp2dneHv728MHjzYuHjxonmMxMREAzB27NhhGIZh7NixI98xASMxMdF8XnBwsOHi4mLY29sbjRo1MjZu3Jgnv3//+9/GM888Y9jZ2Rk+Pj7GpEmTLPp37txpVK1a1bCzszPc3NyM3r17G0lJSXnG2bx5swEY8fHx+d6HW7duGf/85z+NUqVKGcWLFzdeeOEFIyUl5Q/eTRExDMMwGcZD7NAiIiIiIiIiInIf2oNBRERERERERApNBQYRERERERERKTQVGERERERERESk0FRgEBEREREREZFCU4FBRERERERERApNBQYRERERERERKTQVGEREREREisjNmzeJiIjAz88PBwcHGjduzIEDBx7L3PPmzaNFixY4OztjMpm4du1anpiOHTvi6+uLvb09Xl5e9O7dm+Tk5ALHPHfuHCaTKd9jxYoVAPz222+0bdsWb29v7OzsKFeuHEOGDOHGjRvmcVatWkWrVq1wd3fH2dmZoKAgNm/eXOC8kyZNwmQyERERkW+/YRi0a9cOk8nEmjVrLPqGDh1KvXr1sLOzo3bt2gXOISIPpgKDiIiIiEgRGThwIFu3bmXx4sUcOXKE1q1bExISQlJS0l8+d0ZGBm3btuXNN98sMCY4OJjly5cTHx/Pt99+y5kzZ3jppZcKjC9XrhwpKSkWx7vvvkuJEiVo164dAFZWVnTq1Il169Zx6tQpFixYwA8//MDgwYPN4+zatYtWrVqxceNGYmNjCQ4OpkOHDhw+fDjPnAcOHODzzz+nVq1aBeY1Y8YMTCZTgf39+/enW7duBfaLyMMxGYZhFHUSIiIiIiJPmlu3buHk5MTatWtp3769ub1evXq0a9eODz744LHksXPnToKDg7l69SolS5a8b+y6desIDQ0lMzMTGxubhxq/Tp061K1bl8jIyAJjPvnkE6ZOncovv/xSYEz16tXp1q0bb7/9trktLS2NunXr8tlnn/HBBx9Qu3ZtZsyYYXFeXFwczz//PAcPHsTLy4vVq1cTGhqaZ/x33nmHNWvWEBcX91DXJSJ5aQWDiIiIiEgRuHPnDjk5Odjb21u0Ozg4sGfPniLKqmC///47S5cupXHjxg9dXIiNjSUuLo4BAwYUGJOcnMyqVato3rx5gTG5ubncvHkTV1dXi/bw8HDat29PSEhIvudlZGTQs2dPZs+ejaen50PlLCJ/ngoMIiIiIiJFwMnJiaCgIN5//32Sk5PJyclhyZIlxMTEkJKSUtTpmY0aNQpHR0fc3Ny4cOECa9eufehzIyMjqVq1Ko0bN87T16NHD4oXL46Pjw/Ozs58+eWXBY4zbdo00tLS6Nq1q7lt2bJlHDp0iIkTJxZ43vDhw2ncuDGdOnV66JxF5M9TgUFEREREpIgsXrwYwzDw8fHBzs6OTz75hB49emBl9ff53/SRI0dy+PBhtmzZQrFixejTpw8P85T1rVu3iIqKKnD1wscff8yhQ4dYu3YtZ86cYcSIEfnGRUVF8e6777J8+XI8PDwA+OWXXxg2bBhLly7NswLknnXr1rF9+/Y8j0yIyF/HuqgTEBERERF5UlWsWJHo6GjS09O5ceMGXl5edOvWjQoVKhR1amalS5emdOnSVK5cmapVq1KuXDn27t1LUFDQfc9buXIlGRkZ9OnTJ99+T09PPD09CQgIwNXVlaZNmzJu3Di8vLzMMcuWLWPgwIGsWLHC4jGI2NhYLl++TN26dc1tOTk57Nq1i08//ZTMzEy2b9/OmTNn8uwr0blzZ5o2bcrOnTv/+M0QkftSgUFEREREpIg5Ojri6OjI1atX2bx5M1OmTCnqlPKVm5sLQGZm5gNjIyMj6dixI+7u7n9q3K+//pr+/fuzbNkyi00wAVq2bMmRI0cs2vr160dAQACjRo2iWLFijB49moEDB1rE1KxZk48//pgOHTo8MCcR+eNUYBARERERKSKbN2/GMAyqVKlCQkICI0eOJCAggH79+v3lc6emppKamkpCQgIAR44cwcnJCV9fX1xdXdm3bx8HDhzgmWeeoVSpUpw5c4Zx48ZRsWJF8+qFpKQkWrZsyaJFi2jYsKF57ISEBHbt2sXGjRvzzLtx40YuXbpEgwYNKFGiBMeOHWPkyJE0adIEf39/4O5jEWFhYcycOZNGjRqRmpoK3N0A08XFBScnJ2rUqGEx7r19Iu6131sh8d98fX0pX768Ra5paWmkpqZy69Yt81skqlWrhq2t7Z+8uyJPpr/Pw10iIiIiIk+Y69evEx4eTkBAAH369OGZZ55h8+bND/2WhsKYO3cuderUYdCgQQA0a9aMOnXqsG7dOgCKFy/OqlWraNmyJVWqVGHAgAHUqlWL6Oho7OzsAMjOziY+Pp6MjAyLsb/66ivKli1L69at88zr4ODAF198wTPPPEPVqlUZPnw4HTt2ZMOGDeaYefPmcefOHcLDw/Hy8jIfw4YNe+T3YeDAgdSpU4fPP/+cU6dOUadOHerUqUNycvIjn0vkf53JeJgdWkRERERERERE7kMrGERERERERESk0FRgEBEREREREZFCU4FBRERERERERApNBQYRERERERERKTQVGERERERERESk0FRgEBERERF5zHbt2kWHDh3w9vbGZDKxZs2aPDGGYfD222/j5eWFg4MDISEhnD59+pHncuzYMTp37oy/vz8mk4kZM2bcN37SpEmYTCYiIiIKPe6cOXOoVasWzs7OODs7ExQUxPfff28R849//IOKFSvi4OCAu7s7nTp14uTJk+b+BQsWYDKZ8j0uX74MQN++ffPtr169unmcmzdvEhERgZ+fHw4ODjRu3JgDBw7c/+aJiAUVGEREREREHrP09HQCAwOZPXt2gTFTpkzhk08+Ye7cuezbtw9HR0fatGnD7du3H2kuGRkZVKhQgUmTJuHp6Xnf2AMHDvD5559Tq1atRzJu2bJlmTRpErGxsRw8eJBnn32WTp06cezYMXNMvXr1mD9/PidOnGDz5s0YhkHr1q3JyckBoFu3bqSkpFgcbdq0oXnz5nh4eAAwc+ZMi/5ffvkFV1dXunTpYp5n4MCBbN26lcWLF3PkyBFat25NSEgISUlJD7xWEbnLZBiGUdRJiIiIiIg8qUwmE6tXryY0NNTcZhgG3t7evP7667zxxhsAXL9+nTJlyrBgwQK6d+/+l+Ti7+9PREREvqsT0tLSqFu3Lp999hkffPABtWvXfuBqh4cZ97+5uroydepUBgwYkG//zz//TGBgIAkJCVSsWDFP/5UrV/Dx8SEyMpLevXvnO8aaNWt48cUXSUxMxM/Pj1u3buHk5MTatWtp3769Oa5evXq0a9eODz744KGuU+RJpxUMIiIiIiJ/M4mJiaSmphISEmJuc3FxoVGjRsTExBRJTuHh4bRv394ip0cpJyeHZcuWkZ6eTlBQUL4x6enpzJ8/n/Lly1OuXLl8YxYtWkTx4sV56aWXCpwrMjKSkJAQ/Pz8ALhz5w45OTnY29tbxDk4OLBnz54/eUUiTx7rok5AREREREQspaamAlCmTBmL9jJlypj7Hqdly5Zx6NChv2RPgiNHjhAUFMTt27cpUaIEq1evplq1ahYxn332Gf/6179IT0+nSpUqbN26FVtb23zHi4yMpGfPnjg4OOTbn5yczPfff09UVJS5zcnJiaCgIN5//32qVq1KmTJl+Prrr4mJiaFSpUqP7mJF/sdpBYOIiIiIiBTol19+YdiwYSxdujTPL/yPQpUqVYiLi2Pfvn28+uqrhIWFcfz4cYuYXr16cfjwYaKjo6lcuTJdu3bNdy+KmJgYTpw4UeDjFQALFy6kZMmSFo+kACxevBjDMPDx8cHOzo5PPvmEHj16YGWlr0wiD0v/tYiIiIiI/M3c2xTx0qVLFu2XLl164EaMj1psbCyXL1+mbt26WFtbY21tTXR0NJ988gnW1tbmzRb/LFtbWypVqkS9evWYOHEigYGBzJw50yLGxcWFp556imbNmrFy5UpOnjzJ6tWr84z15ZdfUrt2berVq5fvXIZh8NVXX9G7d+88KyAqVqxIdHQ0aWlp/PLLL+zfv5/s7GwqVKhQqOsTeZKowCAiIiIi8jdTvnx5PD092bZtm7ntxo0b7Nu3r8D9Cf4qLVu25MiRI8TFxZmP+vXr06tXL+Li4ihWrNgjnS83N5fMzMwC+w3DwDCMPDFpaWksX778vqsXoqOjSUhIuG+Mo6MjXl5eXL16lc2bN9OpU6c/fhEiTyjtwSAiIiIi8pilpaWRkJBg/pyYmEhcXByurq74+vpiMpmIiIjggw8+4KmnnqJ8+fKMGzcOb2/vPEv7CysrK8v8SEJWVhZJSUnExcVRokQJKlWqhJOTEzVq1LA4x9HRETc3N4v2Pn364OPjw8SJEx9qXIAxY8bQrl07fH19uXnzJlFRUezcuZPNmzcDcPbsWb755htat26Nu7s7Fy9eZNKkSTg4OPDcc89Z5PTNN99w584dXn755QKvNTIykkaNGuW5HsD8CswqVaqQkJDAyJEjCQgIoF+/fn/0loo8sVRgEBERERF5zA4ePEhwcLD584gRIwAICwtjwYIFAOZNDV955RWuXbvGM888w6ZNmx75PgjJycnUqVPH/HnatGlMmzaN5s2bs3Pnzoce58KFCxb7FTzMuJcvX6ZPnz6kpKTg4uJCrVq12Lx5M61atQLA3t6e3bt3M2PGDK5evUqZMmVo1qwZP/30Ex4eHhbzR0ZG8uKLL1KyZMl887t+/Trffvttnscv/rN/zJgxXLx4EVdXVzp37syHH36IjY3NQ98DkSedyTAMo6iTEBEREREREZH/t2kPBhEREREREREpNBUYRERERERERKTQVGAQERERERERkUJTgUFERERERERECk0FBhEREREREREpNBUYREREREQes127dtGhQwe8vb0xmUysWbMmT8yqVato3bo1bm5umEwm4uLi/pJcUlJS6NmzJ5UrV8bKyoqIiIj7xi9btgyTyURoaOh943bu3InJZMpzpKammmPmzJlDrVq1cHZ2xtnZmaCgIL7//nuLcVq0aJFnjMGDB1vEDB06lHr16mFnZ0ft2rXz5HL79m369u1LzZo1sba2zjf3vn375ptv9erV73udIvJ/VGAQEREREXnM0tPTCQwMZPbs2feNeeaZZ5g8efJfmktmZibu7u6MHTuWwMDA+8aeO3eON954g6ZNmz70+PHx8aSkpJgPDw8Pc1/ZsmWZNGkSsbGxHDx4kGeffZZOnTpx7NgxizEGDRpkMcaUKVPyzNO/f3+6deuWbw45OTk4ODgwdOhQQkJC8o2ZOXOmxRy//PILrq6udOnS5aGvVeRJZ13UCYiIiIiIPGnatWtHu3bt7hvTu3dv4O6X+r+Sv78/M2fOBOCrr74qMC4nJ4devXrx7rvvsnv3bq5du/ZQ43t4eFCyZMl8+zp06GDx+cMPP2TOnDns3bvXYuVA8eLF8fT0LHCOTz75BIArV67w888/5+l3dHRkzpw5APz444/55u7i4oKLi4v585o1a7h69Sr9+vUrcF4RsaQVDCIiIiIi8kDvvfceHh4eDBgw4A+dV7t2bby8vGjVqhU//vhjgXE5OTksW7aM9PR0goKCLPqWLl1K6dKlqVGjBmPGjCEjI+NPXcMfERkZSUhICH5+fn/5XCL/K7SCQURERERE7mvPnj1ERkb+oX0gvLy8mDt3LvXr1yczM5Mvv/ySFi1asG/fPurWrWuOO3LkCEFBQdy+fZsSJUqwevVqqlWrZu7v2bMnfn5+eHt78/PPPzNq1Cji4+NZtWrVo7xEC8nJyXz//fdERUX9ZXOI/C9SgUFERERERAp08+ZNevfuzRdffEHp0qUf+rwqVapQpUoV8+fGjRtz5swZPv74YxYvXmwRFxcXx/Xr11m5ciVhYWFER0ebiwyvvPKKObZmzZp4eXnRsmVLzpw5Q8WKFR/BFea1cOFCSpYs+cCNLEXEkgoMIiIiIiJSoDNnznDu3DmL/RJyc3MBsLa2Jj4+/qG/6Dds2JA9e/ZYtNna2lKpUiUA6tWrx4EDB5g5cyaff/55vmM0atQIgISEhL+kwGAYBl999RW9e/fG1tb2kY8v8r9MBQYRERERESlQQEAAR44csWgbO3YsN2/eZObMmZQrV+6hx4qLi8PLy+u+Mbm5uWRmZt53DOCB4/xZ0dHRJCQk/OG9JkREBQYRERERkccuLS2NhIQE8+fExETi4uJwdXXF19cXgN9//50LFy6QnJwM3H3dI4Cnp+d936jwZ9z70p6WlsaVK1eIi4vD1taWatWqYW9vT40aNSzi770V4j/bx4wZQ1JSEosWLQJgxowZlC9fnurVq3P79m2+/PJLtm/fzpYtWyzOadeuHb6+vty8eZOoqCh27tzJ5s2bgburJ6Kionjuuedwc3Pj559/Zvjw4TRr1oxatWqZx0lISCAtLY3U1FRu3bplvp5q1aqZVyEcP36crKwsfv/9d27evGmOqV27tsW1RUZG0qhRozzXLCIPpgKDiIiIiMhjdvDgQYKDg82fR4wYAUBYWBgLFiwAYN26dRavSOzevTsA48eP55133nmk+dSpU8f8d2xsLFFRUfj5+f2hV2SmpKRw4cIF8+esrCxef/11kpKSKF68OLVq1eKHH36wuO7Lly/Tp08fUlJScHFxoVatWmzevJlWrVoBdx+f+OGHH5gxYwbp6emUK1eOzp07M3bsWIu5Bw4cSHR0dJ7rSUxMxN/fH4DnnnuO8+fP54kxDMPcdv36db799lvzaztF5I8xGf/5X5SIiIiIiIiIyJ9gVdQJiIiIiIiIiMj/+1RgEBEREREREZFCU4FBRERERERERApNBQYRERERERERKTQVGERERERERESk0FRgEBERERF5zHbt2kWHDh3w9vbGZDKxZs0ai/7s7GxGjRpFzZo1cXR0xNvbmz59+pCcnPzIczl27BidO3fG398fk8nEjBkz7hs/adIkTCYTERER943Lzs7mvffeo2LFitjb2xMYGMimTZssYu7N+d9HeHi4RVxMTAzPPvssjo6OODs706xZM27dunXfcSZNmmQxxubNm3n66adxcnLC3d2dzp0753kN5+zZs6latSoODg5UqVKFRYsW3fcaRcSSCgwiIiIiIo9Zeno6gYGBzJ49O9/+jIwMDh06xLhx4zh06BCrVq0iPj6ejh07PvJcMjIyqFChApMmTcLT0/O+sQcOHODzzz+nVq1aDxx37NixfP7558yaNYvjx48zePBgXnjhBQ4fPmwxXkpKivnYunUrAF26dDHHxMTE0LZtW1q3bs3+/fs5cOAAQ4YMwcrK8qvMe++9ZzHWa6+9Zu5LTEykU6dOPPvss8TFxbF582Z+/fVXXnzxRXPMnDlzGDNmDO+88w7Hjh3j3XffJTw8nPXr1z/wWkXkLpNhGEZRJyEiIiIi8qQymUysXr2a0NDQ+8YdOHCAhg0bcv78eXx9ff+SXPz9/YmIiMh3dUJaWhp169bls88+44MPPqB27dr3Xe3g7e3NW2+9ZbEaoXPnzjg4OLBkyZJ8z4mIiGDDhg2cPn0ak8kEwNNPP02rVq14//33/1TeACtXrqRHjx5kZmaaCxPr16+nU6dOZGZmYmNjQ+PGjWnSpAlTp041n/f666+zb98+9uzZU+DcIvJ/tIJBREREROT/AdevX8dkMlGyZMkimT88PJz27dsTEhLyUPGZmZnY29tbtDk4OBT4ZT0rK4slS5bQv39/c3Hh8uXL7Nu3Dw8PDxo3bkyZMmVo3rx5vmNMmjQJNzc36tSpw9SpU7lz5465r169elhZWTF//nxycnK4fv06ixcvJiQkBBsbm/vmu3//frKzsx/qmkWedCowiIiIiIj8zd2+fZtRo0bRo0cPnJ2dH/v8y5Yt49ChQ0ycOPGhz2nTpg0fffQRp0+fJjc3l61bt7Jq1SpSUlLyjV+zZg3Xrl2jb9++5razZ88C8M477zBo0CA2bdpE3bp1admyJadPnzbHDR06lGXLlrFjxw7+8Y9/MGHCBP71r3+Z+8uXL8+WLVt48803sbOzo2TJkly8eJHly5db5Pvll18SGxuLYRgcPHiQL7/8kuzsbH799deHvm6RJ5kKDCIiIiIif2PZ2dl07doVwzCYM2fOY5//l19+YdiwYSxdujTPL/z3M3PmTJ566ikCAgKwtbVlyJAh9OvXL8/eCfdERkbSrl07vL29zW25ubkA/OMf/6Bfv37UqVOHjz/+mCpVqvDVV1+Z40aMGEGLFi2oVasWgwcPZvr06cyaNYvMzEwAUlNTGTRoEGFhYRw4cIDo6GhsbW156aWXuPfE+Lhx42jXrh1PP/00NjY2dOrUibCwMIACcxYRS/ovRURERETkb+peceH8+fNs3bq1SFYvxMbGcvnyZerWrYu1tTXW1tZER0fzySefYG1tTU5OTr7nubu7s2bNGtLT0zl//jwnT56kRIkSVKhQIU/s+fPn+eGHHxg4cKBFu5eXFwDVqlWzaK9atSoXLlwoMOdGjRpx584d81siZs+ejYuLC1OmTKFOnTo0a9aMJUuWsG3bNvbt2wfcfRziq6++IiMjg3PnznHhwgX8/f3Nb50QkQezLuoEREREREQkr3vFhdOnT7Njxw7c3NyKJI+WLVty5MgRi7Z+/foREBDAqFGjKFas2H3Pt7e3x8fHh+zsbL799lu6du2aJ2b+/Pl4eHjQvn17i3Z/f3+8vb2Jj4+3aD916hTt2rUrcM64uDisrKzw8PAA7r4p479XIdzL+94qiXtsbGwoW7YscPfRkOeff14rGEQekgoMIiIiIiKPWVpaGgkJCebPiYmJxMXF4erqiq+vL9nZ2bz00kscOnSIDRs2kJOTQ2pqKgCurq7Y2to+slyysrI4fvy4+e+kpCTi4uIoUaIElSpVwsnJiRo1alic4+joiJubm0V7nz598PHxMe/TsG/fPpKSkqhduzZJSUm888475ObmWuyNAHe/4M+fP5+wsDCsrS2/nphMJkaOHMn48eMJDAykdu3aLFy4kJMnT7Jy5Urg7mss9+3bR3BwME5OTsTExDB8+HBefvllSpUqBUD79u35+OOPee+99+jRowc3b97kzTffxM/Pjzp16gB3ixb79++nUaNGXL16lY8++oijR4+ycOHCR3avRf7XqcAgIiIiIvKYHTx4kODgYPPnESNGABAWFsaCBQtISkpi3bp1ANSuXdvi3B07dtCiRYtHlktycrL5SzbAtGnTmDZtGs2bN2fnzp0PPc6FCxcsfum/ffs2Y8eO5ezZs5QoUYLnnnuOxYsX53kLxg8//MCFCxfo379/vuNGRERw+/Zthg8fzu+//05gYCBbt26lYsWKANjZ2bFs2TLeeecdMjMzKV++PMOHDzffU4Bnn32WqKgopkyZwpQpUyhevDhBQUFs2rQJBwcHAHJycpg+fTrx8fHY2NgQHBzMTz/9hL+//0PfA5Enncm4t6uJiIiIiIiIiMifpIeJRERERERERKTQVGAQERERERERkUJTgUFERERERERECk0FBhEREREREREpNBUYRERERERERKTQVGAQEREREXnMdu3aRYcOHfD29sZkMrFmzZo8Me+88w4BAQE4OjpSqlQpQkJC2Ldv3yPP5dixY3Tu3Bl/f39MJhMzZszINxeTyWRxBAQE3HfcVatWUb9+fUqWLImjoyO1a9dm8eLFFjH/Pea9Y+rUqQDs3LmzwJgDBw6Yx1m+fDm1a9emePHi+Pn5mc//z1xatWqFu7s7zs7OBAUFsXnz5jw5JyUl8fLLL+Pm5oaDgwM1a9bk4MGDD3srRZ54KjCIiIiIiDxm6enpBAYGMnv27AJjKleuzKeffsqRI0fYs2cP/v7+tG7dmitXrjzSXDIyMqhQoQKTJk3C09OzwLjq1auTkpJiPvbs2XPfcV1dXXnrrbeIiYnh559/pl+/fvTr18/ii/1/jpeSksJXX32FyWSic+fOADRu3DhPzMCBAylfvjz169cH4Pvvv6dXr14MHjyYo0eP8tlnn/Hxxx/z6aefmufZtWsXrVq1YuPGjcTGxhIcHEyHDh04fPiwOebq1as0adIEGxsbvv/+e44fP8706dMpVarUn7qvIk8ik2EYRlEnISIiIiLypDKZTKxevZrQ0ND7xt24cQMXFxd++OEHWrZs+Zfk4u/vT0REBBERERbt77zzDmvWrCEuLq5Q49etW5f27dvz/vvv59sfGhrKzZs32bZtW7792dnZ+Pj48NprrzFu3DgAevbsSXZ2NitWrDDHzZo1iylTpnDhwgVMJlO+Y1WvXp1u3brx9ttvAzB69Gh+/PFHdu/eXZhLFHmiaQWDiIiIiMjfXFZWFvPmzcPFxYXAwMAiyeH06dN4e3tToUIFevXqxYULFx76XMMw2LZtG/Hx8TRr1izfmEuXLvHdd98xYMCAAsdZt24dv/32G/369TO3ZWZmYm9vbxHn4ODAxYsXOX/+fL7j5ObmcvPmTVxdXS3Grl+/Pl26dMHDw4M6derwxRdfPPQ1iogKDCIiIiIif1sbNmygRIkS2Nvb8/HHH7N161ZKly792PNo1KgRCxYsYNOmTcyZM4fExESaNm3KzZs373ve9evXKVGiBLa2trRv355Zs2bRqlWrfGMXLlyIk5MTL774YoHjRUZG0qZNG8qWLWtua9OmDatWrWLbtm3k5uZy6tQppk+fDtx9BCM/06ZNIy0tja5du5rbzp49y5w5c3jqqafYvHkzr776KkOHDmXhwoX3vUYR+T/WRZ2AiIiIiIjkLzg4mLi4OH799Ve++OILunbtyr59+/Dw8HisebRr1878d61atWjUqBF+fn4sX778visOnJyciIuLIy0tjW3btjFixAgqVKhAixYt8sR+9dVX9OrVK89qhHsuXrzI5s2bWb58uUX7oEGDOHPmDM8//zzZ2dk4OzszbNgw3nnnHays8v6eGhUVxbvvvsvatWst7mNubi7169dnwoQJANSpU4ejR48yd+5cwsLC7nt/ROQurWAQEREREfmbcnR0pFKlSjz99NNERkZibW1NZGRkUadFyZIlqVy5MgkJCfeNs7KyolKlStSuXZvXX3+dl156iYkTJ+aJ2717N/Hx8QwcOLDAsebPn4+bmxsdO3a0aDeZTEyePJm0tDTOnz9PamoqDRs2BKBChQoWscuWLWPgwIEsX76ckJAQiz4vLy+qVatm0Va1atU/9CiIyJNOBQYRERERkf9H5ObmkpmZWdRpkJaWxpkzZ/Dy8vpD5xWUf2RkJPXq1StwfwnDMJg/fz59+vTBxsYm35hixYrh4+ODra0tX3/9NUFBQbi7u5v7v/76a/r168fXX39N+/bt85zfpEkT4uPjLdpOnTqFn5/fH7lEkSeaHpEQEREREXnM0tLSLH79T0xMJC4uDldXV3x9fUlPT+fDDz+kY8eOeHl58euvvzJ79mySkpLo0qXLI80lKyuL48ePm/9OSkoiLi6OEiVKUKlSJQDeeOMNOnTogJ+fH8nJyYwfP55ixYrRo0cP8zh9+vTBx8fHvEJh4sSJ1K9fn4oVK5KZmcnGjRtZvHgxc+bMsZj/xo0brFixwrxvQn62b99OYmJiviscfv31V1auXEmLFi24ffs28+fPZ8WKFURHR5tjoqKiCAsLY+bMmTRq1IjU1FTg7maQLi4uAAwfPpzGjRszYcIEunbtyv79+5k3bx7z5s37M7dV5MlkiIiIiIjIY7Vjxw4DyHOEhYUZhmEYt27dMl544QXD29vbsLW1Nby8vIyOHTsa+/fvf+S5JCYm5ptL8+bNzTHdunUzvLy8DFtbW8PHx8fo1q2bkZCQYDFO8+bNzfkbhmG89dZbRqVKlQx7e3ujVKlSRlBQkLFs2bI883/++eeGg4ODce3atQJz7NGjh9G4ceN8+65cuWI8/fTThqOjo1G8eHGjZcuWxt69e/Pkdr/7fc/69euNGjVqGHZ2dkZAQIAxb968AnMSkbxMhmEYRVDXEBEREREREZH/IdqDQUREREREREQKTQUGERERERERESk0FRhEREREREREpNBUYBARERERERGRQlOBQUREREREREQKTQUGERERERERESk0FRhERERERB6zXbt20aFDB7y9vTGZTKxZs+a+8YMHD8ZkMjFjxoxHnsuxY8fo3Lkz/v7+Bc7xzjvvYDKZLI6AgID7jtuiRYs855hMJtq3b2+Oya/fZDIxdepUc8yhQ4do1aoVJUuWxM3NjVdeeYW0tDRz/7///W969OhBuXLlcHBwoGrVqsycOTNPPpmZmbz11lv4+flhZ2eHv78/X331Vb65L1u2DJPJRGho6APunoj8J+uiTkBERERE5EmTnp5OYGAg/fv358UXX7xv7OrVq9m7dy/e3t5/SS4ZGRlUqFCBLl26MHz48ALjqlevzg8//GD+bG19/68Sq1atIisry/z5t99+IzAwkC5dupjbUlJSLM75/vvvGTBgAJ07dwYgOTmZkJAQunXrxqeffsqNGzeIiIigb9++rFy5EoDY2Fg8PDxYsmQJ5cqV46effuKVV16hWLFiDBkyxDx2165duXTpEpGRkVSqVImUlBRyc3Pz5H3u3DneeOMNmjZtet/rE5G8VGAQEREREXnM2rVrR7t27R4Yl5SUxGuvvcbmzZstfvl/lBo0aECDBg0AGD16dIFx1tbWeHp6PvS4rq6uFp+XLVtG8eLFLQoM/z3e2rVrCQ4OpkKFCgBs2LABGxsbZs+ejZXV3cXXc+fOpVatWiQkJFCpUiX69+9vMUaFChWIiYlh1apV5gLDpk2biI6O5uzZs+a8/P398+Sck5NDr169ePfdd9m9ezfXrl176OsVET0iISIiIiLyt5Sbm0vv3r0ZOXIk1atXL+p0OH36NN7e3lSoUIFevXpx4cKFP3R+ZGQk3bt3x9HRMd/+S5cu8d133zFgwABzW2ZmJra2tubiAoCDgwMAe/bsKXCu69evWxQ41q1bR/369ZkyZQo+Pj5UrlyZN954g1u3blmc99577+Hh4WGRg4g8PBUYRERERET+hiZPnoy1tTVDhw4t6lRo1KgRCxYsYNOmTcyZM4fExESaNm3KzZs3H+r8/fv3c/ToUQYOHFhgzMKFC3FycrJ4ZOTZZ58lNTWVqVOnkpWVxdWrV82rLP778Yp7fvrpJ7755hteeeUVc9vZs2fZs2cPR48eZfXq1cyYMYOVK1fyz3/+0xyzZ88eIiMj+eKLLx7qmkQkLxUYRERERET+ZmJjY5k5cyYLFizAZDIVdTq0a9eOLl26UKtWLdq0acPGjRu5du0ay5cvf6jzIyMjqVmzJg0bNiww5quvvqJXr17Y29ub26pXr87ChQuZPn06xYsXx9PTk/Lly1OmTBmLVQ33HD16lE6dOjF+/Hhat25tbs/NzcVkMrF06VIaNmzIc889x0cffcTChQu5desWN2/epHfv3nzxxReULl36D9wZEflP2oNBRERERORvZvfu3Vy+fBlfX19zW05ODq+//jozZszg3LlzRZccULJkSSpXrkxCQsIDY9PT01m2bBnvvfdegTG7d+8mPj6eb775Jk9fz5496dmzJ5cuXcLR0RGTycRHH31k3qfhnuPHj9OyZUteeeUVxo4da9Hn5eWFj48PLi4u5raqVatiGAYXL14kPT2dc+fO0aFDB3P/vQ0gra2tiY+Pp2LFig+8VpEnnQoMIiIiIiJ/M7179yYkJMSirU2bNvTu3Zt+/foVUVb/Jy0tjTNnztC7d+8Hxq5YsYLMzExefvnlAmMiIyOpV68egYGBBcaUKVMGuLvSwd7enlatWpn7jh07xrPPPktYWBgffvhhnnObNGnCihUrSEtLo0SJEgCcOnUKKysrypYti8lk4siRIxbnjB07lps3bzJz5kzKlSv3wOsUERUYREREREQeu7S0NItf/xMTE4mLi8PV1RVfX1/c3Nxwc3OzOMfGxgZPT0+qVKnySHPJysri+PHj5r+TkpKIi4ujRIkSVKpUCYA33niDDh064OfnR3JyMuPHj6dYsWL06NHDPE6fPn3w8fFh4sSJFuNHRkYSGhqa53ruuXHjBitWrGD69On59n/66ac0btyYEiVKsHXrVkaOHMmkSZMoWbIkcPexiGeffZY2bdowYsQIUlNTAShWrBju7u7A3VUQ77//Pv369ePdd9/l119/ZeTIkfTv39+8aWSNGjUs5r03/n+3i0jBVGAQEREREXnMDh48SHBwsPnziBEjAAgLC2PBggWPNZfk5GTq1Klj/jxt2jSmTZtG8+bN2blzJwAXL16kR48e/Pbbb7i7u/PMM8+wd+9e8xd4gAsXLuTZFyE+Pp49e/awZcuWAudftmwZhmFYFCv+0/79+xk/fjxpaWkEBATw+eefW6ycWLlyJVeuXGHJkiUsWbLE3O7n52d+lOReceK1116jfv36uLm50bVrVz744IOHvk8i8mAmwzCMok5CRERERERERP7fprdIiIiIiIiIiEihqcAgIiIiIiIiIoWmAoOIiIiIiIiIFJoKDCIiIiIiIiJSaCowiIiIiIiIiEihqcAgIiIiIvKY7dq1iw4dOuDt7Y3JZGLNmjV5Yvr27YvJZLI42rZt+8hzOXbsGJ07d8bf3x+TycSMGTPyjUtKSuLll1/Gzc0NBwcHatasycGDBwscd9WqVbRq1Qp3d3ecnZ0JCgpi8+bNFjETJ06kQYMGODk54eHhQWhoKPHx8RYxt2/fJjw8HDc3N0qUKEHnzp25dOmSuX/BggV57tO94/LlywDs2bOHJk2amHMPCAjg448/LvQ1ioglFRhERERERB6z9PR0AgMDmT179n3j2rZtS0pKivn4+uuvH3kuGRkZVKhQgUmTJuHp6ZlvzNWrV2nSpAk2NjZ8//33HD9+nOnTp1OqVKkCx921axetWrVi48aNxMbGEhwcTIcOHTh8+LA5Jjo6mvDwcPbu3cvWrVvJzs6mdevWpKenm2OGDx/O+vXrWbFiBdHR0SQnJ/Piiy+a+7t162Zxj1JSUmjTpg3NmzfHw8MDAEdHR4YMGcKuXbs4ceIEY8eOZezYscybN69Q1ygilkyGYRhFnYSIiIiIyJPKZDKxevVqQkNDLdr79u3LtWvX8l3d8Ffx9/cnIiKCiIgIi/bRo0fz448/snv37kKNX716dbp168bbb7+db/+VK1fw8PAgOjqaZs2acf36ddzd3YmKiuKll14C4OTJk1StWpWYmBiefvrpfMfw8fEhMjKS3r17F5jLiy++iKOjI4sXL36k1yjyJNMKBhERERGRv6mdO3fi4eFBlSpVePXVV/ntt9+KJI9169ZRv359unTpgoeHB3Xq1OGLL774Q2Pk5uZy8+ZNXF1dC4y5fv06gDkmNjaW7OxsQkJCzDEBAQH4+voSExOT7xiLFi2iePHi5oJEfg4fPsxPP/1E8+bNH+k1ijzpVGAQEREREfkbatu2LYsWLWLbtm1MnjyZ6Oho2rVrR05OzmPP5ezZs8yZM4ennnqKzZs38+qrrzJ06FAWLlz40GNMmzaNtLQ0unbtmm9/bm4uERERNGnShBo1agCQmpqKra0tJUuWtIgtU6YMqamp+Y4TGRlJz549cXBwyNNXtmxZ7OzsqF+/PuHh4QwcOPCRXqPIk866qBMQEREREZG8unfvbv67Zs2a1KpVi4oVK7Jz505atmz5WHPJzc2lfv36TJgwAYA6depw9OhR5s6dS1hY2APPj4qK4t1332Xt2rXmfRH+W3h4OEePHmXPnj1/Os+YmBhOnDhhfuzhv+3evZu0tDT27t3L6NGjqVSpEj169AAKf40iohUMIiIiIiL/T6hQoQKlS5cmISHhsc/t5eVFtWrVLNqqVq3KhQsXHnjusmXLGDhwIMuXL7d41OE/DRkyhA0bNrBjxw7Kli1rbvf09CQrK4tr165ZxF+6dCnfDSm//PJLateuTb169fKdp3z58tSsWZNBgwYxfPhw3nnnnUdyjSJylwoMIiIiIiL/D7h48SK//fYbXl5ej33uJk2a5Hl95KlTp/Dz87vveV9//TX9+vXj66+/pn379nn6DcNgyJAhrF69mu3bt1O+fHmL/nr16mFjY8O2bdvMbfHx8Vy4cIGgoCCL2LS0NJYvX86AAQMe6ppyc3PJzMws9DWKyP/RIxIiIiIiIo9ZWlqaxUqExMRE4uLicHV1xdfXl7S0NN599106d+6Mp6cnZ86c4V//+heVKlWiTZs2jzSXrKwsjh8/bv47KSmJuLg4SpQoQaVKlYC7r4ps3LgxEyZMoGvXruzfv5958+ZZvOZxzJgxJCUlsWjRIuDuYxFhYWHMnDmTRo0amfdMcHBwwMXFBbj7WERUVBRr167FycnJHOPi4mKOGzBgACNGjMDV1RVnZ2dee+01goKC8rxB4ptvvuHOnTu8/PLLea5x9uzZ+Pr6EhAQANx9hea0adMYOnSoOeZhrlFEHsAQEREREZHHaseOHQaQ5wgLCzMMwzAyMjKM1q1bG+7u7oaNjY3h5+dnDBo0yEhNTX3kuSQmJuabS/PmzS3i1q9fb9SoUcOws7MzAgICjHnz5ln0h4WFWZzTvHnz+16jYRj59gPG/PnzzTG3bt0y/vnPfxqlSpUyihcvbrzwwgtGSkpKnusICgoyevbsme81fvLJJ0b16tWN4sWLG87OzkadOnWMzz77zMjJyflD1ygi92cyDMN4rBUNEREREREREfmfoz0YRERERERERKTQVGAQERERERERkUJTgUFERERERERECk0FBhEREREREREpNBUYRERERERERKTQVGAQEREREXnMdu3aRYcOHfD29sZkMrFmzZp8406cOEHHjh1xcXHB0dGRBg0acOHChUeaS0pKCj179qRy5cpYWVkRERGRb9y1a9cIDw/Hy8sLOzs7KleuzMaNG+87tmEYTJs2jcqVK2NnZ4ePjw8ffvihuX/VqlW0atUKd3d3nJ2dCQoKYvPmzRZjTJw4kQYNGuDk5ISHhwehoaHEx8dbxNy+fZvw8HDc3NwoUaIEnTt35tKlS+b+BQsWYDKZ8j0uX76cJ+8ff/wRa2trateu/YC7JyL/SQUGEREREZHHLD09ncDAQGbPnl1gzJkzZ3jmmWcICAhg586d/Pzzz4wbNw57e/tHmktmZibu7u6MHTuWwMDAfGOysrJo1aoV586dY+XKlcTHx/PFF1/g4+Nz37GHDRvGl19+ybRp0zh58iTr1q2jYcOG5v5du3bRqlUrNm7cSGxsLMHBwXTo0IHDhw+bY6KjowkPD2fv3r1s3bqV7OxsWrduTXp6ujlm+PDhrF+/nhUrVhAdHU1ycjIvvviiub9bt26kpKRYHG3atKF58+Z4eHhY5Hzt2jX69OlDy5Yt/9B9FBEwGYZhFHUSIiIiIiJPKpPJxOrVqwkNDbVo7969OzY2NixevPix5dKiRQtq167NjBkzLNrnzp3L1KlTOXnyJDY2Ng811okTJ6hVqxZHjx6lSpUqD51D9erV6datG2+//Xa+/VeuXMHDw4Po6GiaNWvG9evXcXd3JyoqipdeegmAkydPUrVqVWJiYnj66afzHcPHx4fIyEh69+5t0de9e3eeeuopihUrxpo1a4iLi3vo3EWedFrBICIiIiLyN5Obm8t3331H5cqVadOmDR4eHjRq1KjARyn+auvWrSMoKIjw8HDKlClDjRo1mDBhAjk5OQWes379eipUqMCGDRsoX748/v7+DBw4kN9//73Ac3Jzc7l58yaurq4Fxly/fh3AHBMbG0t2djYhISHmmICAAHx9fYmJicl3jEWLFlG8eHFzQeKe+fPnc/bsWcaPH1/g/CJSMBUYRERERET+Zi5fvkxaWhqTJk2ibdu2bNmyhRdeeIEXX3yR6Ojox57P2bNnWblyJTk5OWzcuJFx48Yxffp0Pvjgg/uec/78eVasWMGiRYtYsGABsbGxeb7U/6dp06aRlpZG165d8+3Pzc0lIiKCJk2aUKNGDQBSU1OxtbWlZMmSFrFlypQhNTU133EiIyPp2bMnDg4O5rbTp08zevRolixZgrW1dYE5ikjB9F+OiIiIiMjfTG5uLgCdOnVi+PDhANSuXZuffvqJuXPn0rx588eej4eHB/PmzaNYsWLUq1ePpKQkpk6dWuCv/bm5uWRmZrJo0SIqV64M3P1iX69ePeLj4/M8NhEVFcW7777L2rVr8+yLcE94eDhHjx5lz549f/paYmJiOHHihMWjJzk5OfTs2ZN3333XnKuI/HEqMIiIiIiI/M2ULl0aa2trqlWrZtFetWrVQn25/rO8vLywsbGhWLFiFrmkpqaSlZWFra1tvudYW1tbfGGvWrUqABcuXLAoMCxbtoyBAweyYsUKi0cd/tOQIUPYsGEDu3btomzZsuZ2T09PsrKyuHbtmsUqhkuXLuHp6ZlnnC+//JLatWtTr149c9vNmzc5ePAghw8fZsiQIcDdAolhGFhbW7NlyxaeffbZB90mkSeeHpEQEREREfmbsbW1pUGDBnlex3jq1Cn8/Pweez5NmjQhISHBvLLiXi5eXl75FhfunXPnzh3OnDljcQ5gcQ1ff/01/fr14+uvv6Z9+/Z5xjEMgyFDhrB69Wq2b99O+fLlLfrr1auHjY0N27ZtM7fFx8dz4cIFgoKCLGLT0tJYvnw5AwYMsGh3dnbmyJEjxMXFmY/BgwdTpUoV4uLiaNSo0YNukYigFQwiIiIiIo9dWloaCQkJ5s+JiYnExcXh6uqKr68vACNHjqRbt240a9aM4OBgNm3axPr169m5c+cjz+femxLS0tK4cuUKcXFx2NramldQvPrqq3z66acMGzaM1157jdOnTzNhwgSGDh1qHuPTTz9l9erV5i/6ISEh1K1bl/79+zNjxgxyc3MJDw+nVatW5lUNUVFRhIWFMXPmTBo1amTeM8HBwQEXFxfg7mMRUVFRrF27FicnJ3OMi4uLOW7AgAGMGDECV1dXnJ2dee211wgKCsrzBolvvvmGO3fu8PLLL1u0W1lZmfd0uMfDwwN7e/s87SJyH4aIiIiIiDxWO3bsMIA8R1hYmEVcZGSkUalSJcPe3t4IDAw01qxZ85fkk18ufn5+FjE//fST0ahRI8POzs6oUKGC8eGHHxp37twx948fPz7POUlJScaLL75olChRwihTpozRt29f47fffjP3N2/e/IH3Ib9+wJg/f7455tatW8Y///lPo1SpUkbx4sWNF154wUhJSclznUFBQUbPnj0f6p6MHz/eCAwMfKhYEbnLZBiG8VgrGiIiIiIiIiLyP0d7MIiIiIiIiIhIoanAICIiIiIiIiKFpgKDiIiIiIiIiBSaCgwiIiIiIiIiUmgqMIiIiIiIiIhIoanAICIiIiLymO3atYsOHTrg7e2NyWRizZo1eWJMJlO+x9SpUx9pLseOHaNz5874+/tjMpmYMWNGnph7ff99hIeH33fsFStWEBAQgL29PTVr1mTjxo3mvuzsbEaNGkXNmjVxdHTE29ubPn36kJycbDFGx44d8fX1xd7eHi8vL3r37p0n5p6EhAScnJwoWbLkH77Gh/k3EZH7U4FBREREROQxS09PJzAwkNmzZxcYk5KSYnF89dVXmEwmOnfu/EhzycjIoEKFCkyaNAlPT898Yw4cOGCRy9atWwHo0qVLgeP+9NNP9OjRgwEDBnD48GFCQ0MJDQ3l6NGj5nkPHTrEuHHjOHToEKtWrSI+Pp6OHTtajBMcHMzy5cuJj4/n22+/5cyZM7z00kt55svOzqZHjx40bdr0T13jw/ybiMj9mQzDMIo6CRERERGRJ5XJZGL16tWEhobeNy40NJSbN2+ybdu2vywXf39/IiIiiIiIuG9cREQEGzZs4PTp05hMpnxjunXrRnp6Ohs2bDC3Pf3009SuXZu5c+fme86BAwdo2LAh58+fx9fXN9+YdevWERoaSmZmJjY2Nub2UaNGkZycTMuWLYmIiODatWt/+hof9t9ERCxpBYOIiIiIyN/cpUuX+O677xgwYEBRp0JWVhZLliyhf//+BRYXAGJiYggJCbFoa9OmDTExMQWec/36dUwmU55HHO75/fffWbp0KY0bN7YoLmzfvp0VK1Zo9YFIEVOBQURERETkb27hwoU4OTnx4osvFnUqrFmzhmvXrtG3b9/7xqWmplKmTBmLtjJlypCamppv/O3btxk1ahQ9evTA2dnZom/UqFE4Ojri5ubGhQsXWLt2rbnvt99+o2/fvixYsCDPeSLyeKnAICIiIiLyN/fVV1/Rq1cv7O3tizoVIiMjadeuHd7e3o9szOzsbLp27YphGMyZMydP/8iRIzl8+DBbtmyhWLFi9OnTh3tPeg8aNIiePXvSrFmzR5aPiPw51kWdgIiIiIiIFGz37t3Ex8fzzTffFHUqnD9/nh9++IFVq1Y9MNbT05NLly5ZtF26dCnPJov3igvnz59n+/bt+a5CKF26NKVLl6Zy5cpUrVqVcuXKsXfvXoKCgti+fTvr1q1j2rRpABiGQW5uLtbW1sybN4/+/fsX4opF5I9QgUFERERE5G8sMjKSevXqERgYWNSpMH/+fDw8PGjfvv0DY4OCgti2bZvFZopbt24lKCjI/PleceH06dPs2LEDNze3B46bm5sLQGZmJnB3r4ecnBxz/9q1a5k8eTI//fQTPj4+D3tpIvIIqMAgIiIiIvKYpaWlkZCQYP6cmJhIXFwcrq6uFm9PuHHjBitWrGD69Ol/WS5ZWVkcP37c/HdSUhJxcXGUKFGCSpUqmeNyc3OZP38+YWFhWFvn/RrRp08ffHx8mDhxIgDDhg2jefPmTJ8+nfbt27Ns2TIOHjzIvHnzgLvFhZdeeolDhw6xYcMGcnJyzPszuLq6Ymtry759+zhw4ADPPPMMpUqV4syZM4wbN46KFSuaCxVVq1a1yOPgwYNYWVlRo0aNP3SND/tvIiL3YYiIiIiIyGO1Y8cOA8hzhIWFWcR9/vnnhoODg3Ht2rW/LJfExMR8c2nevLlF3ObNmw3AiI+Pz3ec5s2b58l/+fLlRuXKlQ1bW1ujevXqxnfffffAeQFjx44dhmEYxs8//2wEBwcbrq6uhp2dneHv728MHjzYuHjxYoHXM3/+fMPFxeUPX+PD/puISMFMhvH/744iIiIiIiIiIvIn6S0SIiIiIiIiIlJoKjCIiIiIiIiISKGpwCAiIiIiIiIihaYCg4iIiIiIiIgUmgoMIiIiIiIiIlJoKjCIiIiIiDxmu3btokOHDnh7e2MymVizZk2emLS0NIYMGULZsmVxcHCgWrVqzJ0795HnkpKSQs+ePalcuTJWVlZERETkiWnRogUmkynP0b59+4ea48cff8Ta2pratWvn6UtKSuLll1/Gzc0NBwcHatasycGDB/MdZ/DgwZhMJmbMmGHRfurUKTp16kTp0qVxdnbmmWeeYceOHRYxBw4coGXLlpQsWZJSpUrRpk0b/v3vf5v74+PjCQ4OpkyZMtjb21OhQgXGjh1Ldnb2Q12jiKjAICIiIiLy2KWnpxMYGMjs2bMLjBkxYgSbNm1iyZIlnDhxgoiICIYMGcK6deseaS6ZmZm4u7szduxYAgMD841ZtWoVKSkp5uPo0aMUK1aMLl26PHD8a9eu0adPH1q2bJmn7+rVqzRp0gQbGxu+//57jh8/zvTp0ylVqlSe2NWrV7N37168vb3z9D3//PPcuXOH7du3ExsbS2BgIM8//zypqanA3WJN27Zt8fX1Zd++fezZswcnJyfatGljLiDY2NjQp08ftmzZQnx8PDNmzOCLL75g/PjxD7xGEbnLZBiGUdRJiIiIiIg8qUwmE6tXryY0NNSivUaNGnTr1o1x48aZ2+rVq0e7du344IMP/pJcWrRoQe3atfOsEPhvM2bM4O233yYlJQVHR8f7xnbv3p2nnnqKYsWKsWbNGuLi4sx9o0eP5scff2T37t33HSMpKYlGjRqxefNm2rdvT0REhHmlxa+//oq7uzu7du2iadOmANy8eRNnZ2e2bt1KSEgIBw8epEGDBly4cIFy5coBcOTIEWrVqsXp06epVKlSvvOOGDGCAwcOPDA/EblLKxhERERERP6GGjduzLp160hKSsIwDHbs2MGpU6do3bp1UadGZGQk3bt3f2BxYf78+Zw9e7bAVQDr1q2jfv36dOnSBQ8PD+rUqcMXX3xhEZObm0vv3r0ZOXIk1atXzzOGm5sbVapUYdGiRaSnp3Pnzh0+//xzPDw8qFevHgBVqlTBzc2NyMhIsrKyuHXrFpGRkVStWhV/f/98c0tISGDTpk00b978Ie6IiIAKDCIiIiIif0uzZs2iWrVqlC1bFltbW9q2bcvs2bNp1qxZkea1f/9+jh49ysCBA+8bd/r0aUaPHs2SJUuwtrbON+bs2bPMmTOHp556is2bN/Pqq68ydOhQFi5caI6ZPHky1tbWDB06NN8xTCYTP/zwA4cPH8bJyQl7e3s++ugjNm3aZH7UwsnJiZ07d7JkyRIcHBwoUaIEmzZt4vvvv8+TW+PGjbG3t+epp56iadOmvPfee3/k9og80VRgEBERERH5G5o1axZ79+5l3bp1xMbGMn36dMLDw/nhhx+KNK/IyEhq1qxJw4YNC4zJycmhZ8+evPvuu1SuXLnAuNzcXOrWrcuECROoU6cOr7zyCoMGDTJvZhkbG8vMmTNZsGABJpMp3zEMwyA8PBwPDw92797N/v37CQ0NpUOHDqSkpABw69YtBgwYQJMmTdi7dy8//vgjNWrUoH379ty6dctivG+++YZDhw4RFRXFd999x7Rp0/7oLRJ5YuVfShQRERERkSJz69Yt3nzzTVavXm1+U0OtWrWIi4tj2rRphISEFEle6enpLFu27IG/6t+8eZODBw9y+PBhhgwZAtwtJhiGgbW1NVu2bOHZZ5/Fy8uLatWqWZxbtWpVvv32WwB2797N5cuX8fX1Nffn5OTw+uuvM2PGDM6dO8f27dvZsGEDV69exdnZGYDPPvuMrVu3snDhQkaPHk1UVBTnzp0jJiYGK6u7v7FGRUVRqlQp1q5dS/fu3c3j39ujoVq1auTk5PDKK6/w+uuvU6xYsULePZH/fSowiIiIiIj8zWRnZ5OdnW3+MnxPsWLFyM3NLaKsYMWKFWRmZvLyyy/fN87Z2ZkjR45YtH322Wds376dlStXUr58eQCaNGlCfHy8RdypU6fw8/MDoHfv3nmKKW3atKF3797069cPgIyMDIA898rKysp8rzIyMrCysrJYBXHv8/3uZ25uLtnZ2eTm5qrAIPIQVGAQEREREXnM0tLSSEhIMH9OTEwkLi4OV1dXfH19cXZ2pnnz5owcORIHBwf8/PyIjo5m0aJFfPTRR488n3tvdkhLS+PKlSvExcVha2ubZ3VBZGQkoaGhuLm55RljzJgxJCUlsWjRIqysrKhRo4ZFv4eHB/b29hbtw4cPp3HjxkyYMIGuXbuyf/9+5s2bx7x584C7Gzj+91w2NjZ4enpSpUoVAIKCgihVqhRhYWG8/fbbODg48MUXX5CYmGhe/dGqVStGjhxJeHg4r732Grm5uUyaNAlra2uCg4MBWLp0KTY2NtSsWRM7OzsOHjzImDFj6NatGzY2NoW4uyJPDhUYREREREQes4MHD5q/2MLd1yEChIWFsWDBAgCWLVvGmDFj6NWrF7///jt+fn58+OGHDB48+JHnU6dOHfPfsbGxREVF4efnx7lz58zt8fHx7Nmzhy1btuQ7RkpKChcuXPhD8zZo0IDVq1czZswY3nvvPcqXL8+MGTPo1avXQ49RunRpNm3axFtvvcWzzz5LdnY21atXZ+3atQQGBgIQEBDA+vXreffddwkKCsLKyoo6deqwadMmvLy8ALC2tmby5MmcOnUKwzDw8/NjyJAhDB8+/A9dk8iTzGQYhlHUSYiIiIiIiIjI/9v0FgkRERERERERKTQVGERERERERESk0FRgEBEREREREZFCU4FBRERERERERApNBQYRERERERERKTQVGERERERERESk0FRgEBERERF5zHbt2kWHDh3w9vbGZDKxZs2aPDGXLl2ib9++eHt7U7x4cdq2bcvp06cfeS4pKSn07NmTypUrY2VlRURERJ6YFi1aYDKZ8hzt27e/79hLly4lMDCQ4sWL4+XlRf/+/fntt98sYq5du0Z4eDheXl7Y2dlRuXJlNm7caBGTlJTEyy+/jJubGw4ODtSsWZODBw8CkJ2dzahRo6hZsyaOjo54e3vTp08fkpOTzeefO3eOAQMGUL58eRwcHKhYsSLjx48nKyvLHHP79m369u1LzZo1sba2JjQ09A/eSRFRgUFERERE5DFLT08nMDCQ2bNn59tvGAahoaGcPXuWtWvXcvjwYfz8/AgJCSE9Pf2R5pKZmYm7uztjx44lMDAw35hVq1aRkpJiPo4ePUqxYsXo0qVLgeP++OOP9OnThwEDBnDs2DFWrFjB/v37GTRokDkmKyuLVq1ace7cOVauXEl8fDxffPEFPj4+5pirV6/SpEkTbGxs+P777zl+/DjTp0+nVKlSAGRkZHDo0CHGjRvHoUOHWLVqFfHx8XTs2NE8xsmTJ8nNzeXzzz/n2LFjfPzxx8ydO5c333zTHJOTk4ODgwNDhw4lJCTkT99PkSeZyTAMo6iTEBERERF5UplMJlavXm3xi/mpU6eoUqUKR48epXr16gDk5ubi6enJhAkTGDhw4F+SS4sWLahduzYzZsy4b9yMGTN4++23SUlJwdHRMd+YadOmMWfOHM6cOWNumzVrFpMnT+bixYsAzJ07l6lTp3Ly5ElsbGzyHWf06NH8+OOP7N69+6Gv48CBAzRs2JDz58/j6+ubb8zUqVOZM2cOZ8+ezdPXt29frl27lu/KEhEpmFYwiIiIiIj8zWRmZgJgb29vbrOyssLOzo49e/YUVVpmkZGRdO/evcDiAkBQUBC//PILGzduxDAMLl26xMqVK3nuuefMMevWrSMoKIjw8HDKlClDjRo1mDBhAjk5ORYx9evXp0uXLnh4eFCnTh2++OKL++Z3/fp1TCYTJUuWvG+Mq6vrw1+0iDyQCgwiIiIiIn8zAQEB+Pr6MmbMGK5evUpWVpb5l/+UlJQizW3//v0cPXr0gasomjRpwtKlS+nWrRu2trZ4enri4uJi8VjI2bNnWblyJTk5OWzcuJFx48Yxffp0PvjgA4uYOXPm8NRTT7F582ZeffVVhg4dysKFC/Od9/bt24waNYoePXrg7Oycb0xCQgKzZs3iH//4x5+4AyJSEBUYRERERET+ZmxsbFi1ahWnTp3C1dWV4sWLs2PHDtq1a4eVVdH+L3xkZCQ1a9akYcOG9407fvw4w4YN4+233yY2NpZNmzZx7tw5Bg8ebI7Jzc3Fw8ODefPmUa9ePbp168Zbb73F3LlzLWLq1q3LhAkTqFOnDq+88gqDBg2yiLknOzubrl27YhgGc+bMyTevpKQk2rZtS5cuXSz2gxCRwrMu6gRERERERCSvevXqERcXx/Xr18nKysLd3Z1GjRpRv379IsspPT2dZcuW8d577z0wduLEiTRp0oSRI0cCUKtWLRwdHWnatCkffPABXl5eeHl5YWNjQ7FixcznVa1aldTUVLKysrC1tcXLy4tq1apZjF21alW+/fZbi7Z7xYXz58+zffv2fFcvJCcnExwcTOPGjZk3b96fuQUich9awSAiIiIi8jfm4uKCu7s7p0+f5uDBg3Tq1KnIclmxYgWZmZm8/PLLD4zNyMjIs9riXiHh3j7zTZo0ISEhgdzcXHPMqVOn8PLywtbW1hwTHx9vMc6pU6fw8/Mzf75XXDh9+jQ//PADbm5uefJJSkqiRYsW1KtXj/nz5xf5ShCR/0VawSAiIiIi8pilpaWRkJBg/pyYmEhcXByurq7mtx6sWLECd3d3fH19OXLkCMOGDSM0NJTWrVs/8nzi4uLMeV25coW4uDhsbW3zrByIjIwkNDQ03y/wY8aMISkpiUWLFgHQoUMHBg0axJw5c2jTpg0pKSlERETQsGFDvL29AXj11Vf59NNPGTZsGK+99hqnT59mwoQJDB061Dzu8OHDady4MRMmTKBr167s37+fefPmmVcgZGdn89JLL3Ho0CE2bNhATk4OqampALi6umJra2suLvj5+TFt2jSuXLliHt/T09P89/Hjx8nKyuL333/n5s2b5vtSu3btwt1gkSeEXlMpIiIiIvKY7dy5k+Dg4DztYWFhLFiwAIBPPvmEqVOncunSJby8vOjTpw/jxo0z/7L/KJlMpjxtfn5+nDt3zvw5Pj6egIAAtmzZQqtWrfLE9+3bl3PnzrFz505z26xZs5g7dy6JiYmULFmSZ599lsmTJ+Pj42OOiYmJYfjw4cTFxeHj48OAAQMYNWqUxWMTGzZsYMyYMZw+fZry5cszYsQI8/4J586do3z58vle144dO2jRogULFiygX79++cb859chf39/zp8/f98YESmYCgwiIiIiIiIiUmh68EhERERERERECk0FBhEREREREREpNBUYRERERERERKTQVGAQERERERERkUJTgUFERERERERECk0FBhERERGRx2zixIk0aNAAJycnPDw8CA0NJT4+3iLm9u3bhIeH4+bmRokSJejcuTOXLl165Lns2rWLDh064O3tjclkYs2aNXli0tLSGDJkCGXLlsXBwYFq1aoxd+7c+477xRdf0LRpU0qVKkWpUqUICQlh//79eeJOnDhBx44dcXFxwdHRkQYNGnDhwgXg7isoTSZTvseKFSvMY+TXv2zZMnP/zp07841JTU01x9y8eZOIiAj8/PxwcHCgcePGHDhw4I/eTpEnmgoMIiIiIiKPWXR0NOHh4ezdu5etW7eSnZ1N69atSU9PN8cMHz6c9evXs2LFCqKjo0lOTubFF1985Lmkp6cTGBjI7NmzC4wZMWIEmzZtYsmSJZw4cYKIiAiGDBnCunXrCjxn586d9OjRgx07dhATE0O5cuVo3bo1SUlJ5pgzZ87wzDPPEBAQwM6dO/n5558ZN24c9vb2AJQrV46UlBSL491336VEiRK0a9fOYr758+dbxIWGhubJKT4+3iLGw8PD3Ddw4EC2bt3K4sWLOXLkCK1btyYkJMQiXxG5P5NhGEZRJyEiIiIi8iS7cuUKHh4eREdH06xZM65fv467uztRUVG89NJLAJw8eZKqVasSExPD008//ZfkYTKZWL16dZ4v5zVq1KBbt26MGzfO3FavXj3atWvHBx988FBj5+TkUKpUKT799FP69OkDQPfu3bGxsWHx4sUPnWOdOnWoW7cukZGRD8z7np07dxIcHMzVq1cpWbJknv5bt27h5OTE2rVrad++/Z++RpEnnVYwiIiIiIgUsevXrwPg6uoKQGxsLNnZ2YSEhJhjAgIC8PX1JSYm5rHn17hxY9atW0dSUhKGYbBjxw5OnTpF69atH3qMjIwMsrOzzdeYm5vLd999R+XKlWnTpg0eHh40atQo30c07omNjSUuLo4BAwbk6QsPD6d06dI0bNiQr776ivx+R61duzZeXl60atWKH3/80dx+584dcnJyzCsn7nFwcGDPnj0PfY0iTzoVGEREREREilBubi4RERE0adKEGjVqAJCamoqtrW2eX9vLlCljsW/A4zJr1iyqVatG2bJlsbW1pW3btsyePZtmzZo99BijRo3C29vbXDS5fPkyaWlpTJo0ibZt27JlyxZeeOEFXnzxRaKjo/MdIzIykqpVq9K4cWOL9vfee4/ly5ezdetWOnfuzD//+U9mzZpl7vfy8mLu3Ll8++23fPvtt5QrV44WLVpw6NAhAJycnAgKCuL9998nOTmZnJwclixZQkxMDCkpKX/0dok8sayLOgERERERkSdZeHg4R48e/Vv/Uj5r1iz27t3LunXr8PPzY9euXYSHh1sUDO5n0qRJLFu2jJ07d5pXCeTm5gLQqVMnhg8fDtxdYfDTTz8xd+5cmjdvbjHGrVu3iIqKsnhM457/bKtTpw7p6elMnTqVoUOHAlClShWqVKlijmncuDFnzpzh448/Nj+esXjxYvr374+Pjw/FihWjbt269OjRg9jY2D9yq0SeaFrBICIiIiJSRIYMGcKGDRvYsWMHZcuWNbd7enqSlZXFtWvXLOIvXbqEp6fnY83x1q1bvPnmm3z00Ud06NCBWrVqMWTIELp168a0adMeeP60adOYNGkSW7ZsoVatWub20qVLY21tTbVq1Sziq1atan6LxH9auXIlGRkZ5v0b7qdRo0ZcvHiRzMzMAmMaNmxIQkKC+XPFihWJjo4mLS2NX375hf3795OdnU2FChUeOJ+I3KUCg4iIiIjIY2YYBkOGDGH16tVs376d8uXLW/TXq1cPGxsbtm3bZm6Lj4/nwoULBAUFPdZcs7Ozyc7OxsrK8qtDsWLFzKsQCjJlyhTef/99Nm3aRP369S36bG1tadCgQZ7Xc546dQo/P788Y0VGRtKxY0fc3d0fmHNcXBylSpXCzs7uvjFeXl552h0dHfHy8uLq1ats3ryZTp06PXA+EblLj0iIiIiIiDxm4eHhREVFsXbtWpycnMz7Kri4uODg4ICLiwsDBgxgxIgRuLq64uzszGuvvUZQUNAjf4NEWlqaxS/5iYmJxMXF4erqiq+vL87OzjRv3pyRI0fi4OCAn58f0dHRLFq0iI8++sh8Xp8+ffDx8WHixIkATJ48mbfffpuoqCj8/f3N11iiRAlKlCgBwMiRI+nWrRvNmjUjODiYTZs2sX79enbu3GmRY0JCArt27WLjxo158l+/fj2XLl3i6aefxt7enq1btzJhwgTeeOMNc8yMGTMoX7481atX5/bt23z55Zds376dLVu2mGM2b96MYRhUqVKFhIQERo4cSUBAAP369Sv8TRZ5UhgiIiIiIvJYAfke8+fPN8fcunXL+Oc//2mUKlXKKF68uPHCCy8YKSkpjzyXHTt25JtLWFiYOSYlJcXo27ev4e3tbdjb2xtVqlQxpk+fbuTm5ppjmjdvbnGOn59fvuOOHz/eYv7IyEijUqVKhr29vREYGGisWbMmT45jxowxypUrZ+Tk5OTp+/77743atWsbJUqUMBwdHY3AwEBj7ty5FrGTJ082KlasaNjb2xuurq5GixYtjO3bt1uM88033xgVKlQwbG1tDU9PTyM8PNy4du3aH7ybIk82k2Hk8/4WEREREREREZE/QHswiIiIiIiIiEihqcAgIiIiIiIiIoWmAoOIiIiIiIiIFJoKDCIiIiIiIiJSaCowiIiIiIiIiEihqcAgIiIiIvKYTZw4kQYNGuDk5ISHhwehoaHEx8dbxMybN48WLVrg7OyMyWTi2rVrf0kux44do3Pnzvj7+2MymZgxY0aemJycHMaNG0f58uVxcHCgYsWKvP/++9zvhXR9+/bFZDLlOapXr24RN3v2bPz9/bG3t6dRo0bs37/fov/MmTO88MILuLu74+zsTNeuXbl06ZJFzO+//06vXr1wdnamZMmSDBgwgLS0NHN/fHw8wcHBlClTBnt7eypUqMDYsWPJzs62GGfFihUEBARgb29PzZo12bhx48PeRhFBBQYRERERkccuOjqa8PBw9u7dy9atW8nOzqZ169akp6ebYzIyMmjbti1vvvnmX5pLRkYGFSpUYNKkSXh6euYbM3nyZObMmcOnn37KiRMnmDx5MlOmTGHWrFkFjjtz5kxSUlLMxy+//IKrqytdunQxx3zzzTeMGDGC8ePHc+jQIQIDA2nTpg2XL18GID09ndatW2Mymdi+fTs//vgjWVlZdOjQgdzcXPM4vXr14tixY2zdupUNGzawa9cuXnnlFXO/jY0Nffr0YcuWLcTHxzNjxgy++OILxo8fb4756aef6NGjBwMGDODw4cOEhoYSGhrK0aNH//S9FXnSmIz7lR1FREREROQvd+XKFTw8PIiOjqZZs2YWfTt37iQ4OJirV69SsmTJvzQPf39/IiIiiIiIsGh//vnnKVOmDJGRkea2zp074+DgwJIlSx5q7DVr1vDiiy+SmJiIn58fAI0aNaJBgwZ8+umnAOTm5lKuXDlee+01Ro8ezZYtW2jXrh1Xr17F2dkZgOvXr1OqVCm2bNlCSEgIJ06coFq1ahw4cID69esDsGnTJp577jkuXryIt7d3vvmMGDGCAwcOsHv3bgC6detGeno6GzZsMMc8/fTT1K5dm7lz5z7UNYo86bSCQURERESkiF2/fh0AV1fXIs4kf40bN2bbtm2cOnUKgH//+9/s2bOHdu3aPfQYkZGRhISEmIsLWVlZxMbGEhISYo6xsrIiJCSEmJgYADIzMzGZTNjZ2Zlj7O3tsbKyYs+ePQDExMRQsmRJc3EBICQkBCsrK/bt25dvLgkJCWzatInmzZub22JiYixyAWjTpo05FxF5MBUYRERERESKUG5uLhERETRp0oQaNWoUdTr5Gj16NN27dycgIAAbGxvq1KlDREQEvXr1eqjzk5OT+f777xk4cKC57ddffyUnJ4cyZcpYxJYpU4bU1FTg7goCR0dHRo0aRUZGBunp6bzxxhvk5OSQkpICQGpqKh4eHhZjWFtb4+rqah7nnsaNG2Nvb89TTz1F06ZNee+998x9qamp981FRB5MBQYRERERkSIUHh7O0aNHWbZsWVGnUqDly5ezdOlSoqKiOHToEAsXLmTatGksXLjwoc5fuHAhJUuWJDQ09A/N6+7uzooVK1i/fj0lSpTAxcWFa9euUbduXays/vhXmW+++YZDhw4RFRXFd999x7Rp0/7wGCJSMOuiTkBERERE5Ek1ZMgQ86aEZcuWLep0CjRy5EjzKgaAmjVrcv78eSZOnEhYWNh9zzUMg6+++orevXtja2trbi9dujTFihXL80aIS5cuWWw22bp1a86cOcOvv/6KtbU1JUuWxNPTkwoVKgDg6elp3hTynjt37vD777/n2bSyXLlyAFSrVo2cnBxeeeUVXn/9dYoVK4anp+cDcxGR+9MKBhERERGRx8wwDIYMGcLq1avZvn075cuXL+qU7isjIyPPioFixYpZvMmhINHR0SQkJDBgwACLdltbW+rVq8e2bdvMbbm5uWzbto2goKA845QuXZqSJUuyfft2Ll++TMeOHQEICgri2rVrxMbGmmO3b99Obm4ujRo1KjCv3NxcsrOzzdcQFBRkkQvA1q1b881FRPKnFQwiIiIiIo9ZeHg4UVFRrF27FicnJ/Nz/i4uLjg4OAB39wRITU0lISEBgCNHjuDk5ISvr+8j3QwyKyuL48ePm/9OSkoiLi6OEiVKUKlSJQA6dOjAhx9+iK+vL9WrV+fw4cN89NFH9O/f3zzOmDFjSEpKYtGiRRbjR0ZG0qhRo3z3lxgxYgRhYWHUr1+fhg0bMmPGDNLT0+nXr585Zv78+VStWhV3d3diYmIYNmwYw4cPp0qVKgBUrVqVtm3bMmjQIObOnUt2djZDhgyhe/fu5jdILF26FBsbG2rWrImdnR0HDx5kzJgxdOvWDRsbGwCGDRtG8+bNmT59Ou3bt2fZsmUcPHiQefPmPbJ7LfI/zxARERERkccKyPeYP3++OWb8+PEPjHkUEhMT852nefPm5pgbN24Yw4YNM3x9fQ17e3ujQoUKxltvvWVkZmaaY8LCwizOMQzDuHbtmuHg4GDMmzevwPlnzZpl+Pr6Gra2tkbDhg2NvXv3WvSPGjXKKFOmjGFjY2M89dRTxvTp043c3FyLmN9++83o0aOHUaJECcPZ2dno16+fcfPmTXP/smXLjLp16xolSpQwHB0djWrVqhkTJkwwbt26ZTHO8uXLjcqVKxu2trZG9erVje++++5hb6OIGIZhMgzDePxlDRERERERERH5X6I9GERERERERESk0FRgEBEREREREZFCU4FBRERERERERApNBQYRERERERERKTQVGERERERERESk0FRgEBERERF5zCZOnEiDBg1wcnLCw8OD0NBQ4uPjzf2///47r732GlWqVMHBwQFfX1+GDh3K9evXH3kuu3btokOHDnh7e2MymVizZk2emEuXLtG3b1+8vb0pXrw4bdu25fTp0/cd99ixY3Tu3Bl/f39MJhMzZsz4U3MbhsHbb7+Nl5cXDg4OhISE5Jm7Y8eO+Pr6Ym9vj5eXF7179yY5OTnPONOmTaNy5crY2dnh4+PDhx9+aBGzdOlSAgMDKV68OF5eXvTv35/ffvvtvtcpIv9HBQYRERERkccsOjqa8PBw9u7dy9atW8nOzqZ169akp6cDkJycTHJyMtOmTePo0aMsWLCATZs2MWDAgEeeS3p6OoGBgcyePTvffsMwCA0N5ezZs6xdu5bDhw/j5+dHSEiIOd/8ZGRkUKFCBSZNmoSnp+efmhtgypQpfPLJJ8ydO5d9+/bh6OhImzZtuH37tjkmODiY5cuXEx8fz7fffsuZM2d46aWXLMYZNmwYX375JdOmTePkyZOsW7eOhg0bmvt//PFH+vTpw4ABAzh27BgrVqxg//79DBo0qMDcRMSSyTAMo6iTEBERERF5kl25cgUPDw+io6Np1qxZvjErVqzg5ZdfJj09HWtr678kD5PJxOrVqwkNDTW3nTp1iipVqnD06FGqV68OQG5uLp6enkyYMIGBAwc+cFx/f38iIiKIiIj4Q3MbhoG3tzevv/46b7zxBgDXr1+nTJkyLFiwgO7du+c71rp16wgNDSUzMxMbGxtOnDhBrVq1OHr0KFWqVMn3nGnTpjFnzhzOnDljbps1axaTJ0/m4sWLD7xGEdEKBhERERGRInfv0QdXV9f7xjg7O/9lxYWCZGZmAmBvb29us7Kyws7Ojj179vylcycmJpKamkpISIi5zcXFhUaNGhETE5PvOb///jtLly6lcePG2NjYALB+/XoqVKjAhg0bKF++PP7+/gwcOJDff//dfF5QUBC//PILGzduxDAMLl26xMqVK3nuuef+0msU+V+iAoOIiIiISBHKzc0lIiKCJk2aUKNGjXxjfv31V95//31eeeWVx5wdBAQE4Ovry5gxY7h69SpZWVnmX/VTUlL+0rlTU1MBKFOmjEV7mTJlzH33jBo1CkdHR9zc3Lhw4QJr16419509e5bz58+zYsUKFi1axIIFC4iNjbV4jKJJkyYsXbqUbt26YWtri6enJy4uLvd9fENELKnAICIiIiJShMLDwzl69CjLli3Lt//GjRu0b9+eatWq8c477zze5AAbGxtWrVrFqVOncHV1pXjx4uzYsYN27dphZfX3+ToxcuRIDh8+zJYtWyhWrBh9+vTh3tPgubm5ZGZmsmjRIpo2bUqLFi2IjIxkx44d5s01jx8/zrBhw3j77beJjY1l06ZNnDt3jsGDBxflZYn8P+Xxrq8SERERERGzIUOGsGHDBnbt2kXZsmXz9N+8eZO2bdvi5OTE6tWrzUv+H7d69eoRFxfH9evXycrKwt3dnUaNGlG/fv2/dN57m0NeunQJLy8vc/ulS5eoXbu2RWzp0qUpXbo0lStXpmrVqpQrV469e/cSFBSEl5cX1tbWVK5c2RxftWpVAC5cuECVKlWYOHEiTZo0YeTIkQDUqlULR0dHmjZtygcffGAxv4jk7+9TchQREREReUIYhsGQIUNYvXo127dvp3z58nlibty4QevWrbG1tWXdunUWeyAUFRcXF9zd3Tl9+jQHDx6kU6dOf+l85cuXx9PTk23btpnbbty4wb59+wgKCirwvNzcXOD/9o9o0qQJd+7csdjA8dSpUwD4+fkBd9968d8rMooVKwaA9sUXeThawSAiIiIi8piFh4cTFRXF2rVrcXJyMu8n4OLigoODg7m4kJGRwZIlS7hx4wY3btwAwN3d3fzF91FIS0sjISHB/DkxMZG4uDhcXV3x9fUF7r7Bwt3dHV9fX44cOcKwYcMIDQ2ldevW5vP69OmDj48PEydOBCArK4vjx4+b/05KSiIuLo4SJUpQqVKlh5rbZDIRERHBBx98wFNPPUX58uUZN24c3t7e5rdN7Nu3jwMHDvDMM89QqlQpzpw5w7hx46hYsaK5CBESEkLdunXp378/M2bMIDc3l/DwcFq1amVe1dChQwcGDRrEnDlzaNOmDSkpKURERNCwYUO8vb0f2f0W+Z9miIiIiIjIYwXke8yfP98wDMPYsWNHgTGJiYmPNJeC5goLCzPHzJw50yhbtqxhY2Nj+Pr6GmPHjjUyMzMtxmnevLnFOYmJifmO27x58z80d25urjFu3DijTJkyhp2dndGyZUsjPj7e3P/zzz8bwcHBhqurq2FnZ2f4+/sbgwcPNi5evGiRX1JSkvHiiy8aJUqUMMqUKWP07dvX+O233yxiPvnkE6NatWqGg4OD4eXlZfTq1SvPOCJSMJNhaL2PiIiIiIiIiBSO9mAQERERERERkUJTgUFERERERERECk0FBhEREREREREpNBUYRERERERERKTQVGAQERERERERkUJTgUFERERE5DGbOHEiDRo0wMnJCQ8PD0JDQ4mPj7eI+cc//kHFihVxcHDA3d2dTp06cfLkyUeey65du+jQoQPe3t6YTCbWrFmTJ+bSpUv07dsXb29vihcvTtu2bTl9+vR9x12wYAEmk8nisLe3t4hZtWoVrVu3xs3NDZPJRFxcXJ5x5s2bR4sWLXB2dsZkMnHt2jWL/p07d+aZ595x4MABc9zPP/9M06ZNsbe3p1y5ckyZMiXPXNeuXSM8PBwvLy/s7OyoXLkyGzduvO91isj/UYFBREREROQxi46OJjw8nL1797J161ays7Np3bo16enp5ph69eoxf/58Tpw4webNmzEMg9atW5OTk/NIc0lPTycwMJDZs2fn228YBqGhoZw9e5a1a9dy+PBh/Pz8CAkJscg3P87OzqSkpJiP8+fP55n7mWeeYfLkyQWOkZGRQdu2bXnzzTfz7W/cuLHFHCkpKQwcOJDy5ctTv359AG7cuEHr1q3x8/MjNjaWqVOn8s477zBv3jzzOFlZWbRq1Ypz586xcuVK4uPj+eKLL/Dx8bnvNYrI/zEZhmEUdRIiIiIiIk+yK1eu4OHhQXR0NM2aNcs35ueffyYwMJCEhAQqVqz4l+RhMplYvXo1oaGh5rZTp05RpUoVjh49SvXq1QHIzc3F09OTCRMmMHDgwHzHWrBgAREREXlWHOTn3LlzlC9fnsOHD1O7du18Y3bu3ElwcDBXr16lZMmSBY6VnZ2Nj48Pr732GuPGjQNgzpw5vPXWW6SmpmJrawvA6NGjWbNmjXlVyNy5c5k6dSonT57ExsbmgTmLSF5awSAiIiIiUsSuX78OgKura7796enpzJ8/n/Lly1Ou3P/X3p2HVVXt/wN/H2SeBxlFQEUBQ5xlMocgcEhB/WZpiZjadDBRr3m9appeBdRKLa6lV8FSszRwIMNQBlNx9iQ4IE6QCCQqlKBy5KzfH/7Y1x0IGAj35vv1PPt5PGt99tqfvQ49T/tz1t67bXOmhvv37wOA7PYGLS0t6Onp4cCBA3Xue+fOHTg7O6Nt27YICQnBmTNnnmquALBz507cvHkTEyZMkNoyMzPRr18/qbgAAMHBwcjJycHt27el/Xx9faFUKmFrawtPT08sWbKkyVeMEP2VscBARERERNSCNBoNIiMj4e/vD09PT1nfv/71LxgbG8PY2Bg//PADUlJSZBfJzcHd3R1OTk6YPXs2bt++jcrKSsTExODatWsoLCx87H5ubm5Yv349duzYgY0bN0Kj0cDPzw/Xrl17qvmuW7cOwcHBcHR0lNqKiopga2sri6v+XFRUBAC4fPkytm3bhqqqKuzevRvz5s3DRx99hH/+859PNV+ivxIWGIiIiIiIWpBSqUR2dja2bNlSo++1117DqVOnkJGRgU6dOmH06NG4d+9es+ano6ODhIQEXLhwAZaWljA0NERaWhoGDx4MLa3HX074+voiLCwM3bp1Q//+/ZGQkABra2t88cUXTy3Xa9euYc+ePZg4ceIT76vRaGBjY4M1a9agZ8+eeOWVVzBnzhx8/vnnTyFTor8m7ZZOgIiIiIjoWRUREYGkpCTs379f9ot7NTMzM5iZmaFjx47w8fGBhYUFEhMTMWbMmGbNs2fPnlCpVCgrK0NlZSWsra3h7e0tPUSxIXR0dNC9e3dcvHjxqeUZFxcHKysrDB8+XNZuZ2eH4uJiWVv1Zzs7OwCAvb09dHR00KpVKynGw8MDRUVFqKysbPaVI0T/i7iCgYiIiIiomQkhEBERgcTERKSmpqJdu3YN2kcIIT0ToSWYmZnB2toaubm5OH78OEJCQhq8b1VVFbKysmBvb/9UchNCIC4uDmFhYTUe0ujr64v9+/dDrVZLbSkpKXBzc4OFhQUAwN/fHxcvXoRGo5FiLly4AHt7exYXiBqIBQYiIiIiomamVCqxceNGbN68GSYmJigqKkJRURHu3r0L4OHzAKKionDixAnk5+fj0KFDePnll2FgYIAhQ4Y0aS537tyBSqWCSqUCAFy5cgUqlQr5+flSzNatW5Geni69qvLFF19EaGgogoKCpJiwsDDMnj1b+rxw4UL8+OOPuHz5Mk6ePInXX38deXl5srdO3Lp1CyqVCmfPngUA5OTkQKVSSc9FAB4+I0GlUkkrH7KysqBSqXDr1i3ZeaSmpuLKlSu1vtVi7Nix0NXVxcSJE3HmzBl88803WLlyJaZPny7FvPPOO7h16xamTp2KCxcu4Pvvv8eSJUugVCr/zLQSPZsEERERERE1KwC1bnFxcUIIIQoKCsTgwYOFjY2N0NHREY6OjmLs2LHi/PnzTZ5LWlparbmMHz9eilm5cqVwdHQUOjo6wsnJScydO1fcv39fNk7//v1l+0RGRgonJyehq6srbG1txZAhQ8TJkydl+8TFxdV67Pnz50sx8+fPr3Ouqo0ZM0b4+fk99jx//vln0bdvX6GnpyfatGkjoqOja8QcOnRIeHt7Cz09PdG+fXuxePFi8eDBg/onkYiEEEIohBCiWSsaRERERERERPSXw1skiIiIiIiIiKjRWGAgIiIiIiIiokZjgYGIiIiIiIiIGo0FBiIiIiIiIiJqNBYYiIiIiIiIiKjRWGAgIiIiIiIiokZjgYGIiIiIqJlFRUWhd+/eMDExgY2NDUJDQ5GTk1NrrBACgwcPhkKhwPbt25s8l/3792PYsGFwcHB47DGKi4sRHh4OBwcHGBoaYtCgQcjNza1zXLVajYULF6JDhw7Q19dH165dkZyc/MTHXrBgAdzd3WFkZAQLCwsEBgbiyJEjsphbt27htddeg6mpKczNzTFx4kTcuXOn1rwuXrwIExMTmJuby9oTEhLQq1cvmJubw8jICN26dcNXX31V5zkSkRwLDEREREREzSwjIwNKpRKHDx9GSkoK1Go1goKCUF5eXiN2xYoVUCgUTy2X8vJydO3aFbGxsbX2CyEQGhqKy5cvY8eOHTh16hScnZ0RGBhYa77V5s6diy+++AKffvopzp49i7fffhsjRozAqVOnGnxsAOjUqRM+++wzZGVl4cCBA3BxcUFQUBBu3Lghxbz22ms4c+YMUlJSkJSUhP379+PNN9+sMZZarcaYMWPw/PPP1+iztLTEnDlzkJmZidOnT2PChAmYMGEC9uzZ89jciEhOIYQQLZ0EEREREdGz7MaNG7CxsUFGRgb69esntatUKrz00ks4fvw47O3tkZiYiNDQ0KeWh0KhqHGMCxcuwM3NDdnZ2XjuuecAABqNBnZ2dliyZAkmTZpU61gODg6YM2cOlEql1DZq1CgYGBhg48aNDTp2bX777TeYmZlh7969CAgIwLlz59C5c2ccO3YMvXr1AgAkJydjyJAhuHbtGhwcHKR9Z82ahevXryMgIACRkZEoLS2t81g9evTA0KFDsWjRojrjiOghrmAgIiIiImphZWVlAB7+il6toqICY8eORWxsLOzs7FoqNdy/fx8AoK+vL7VpaWlBT08PBw4cqHO/R/cBAAMDgzr3qU9lZSXWrFkDMzMzdO3aFQCQmZkJc3NzqbgAAIGBgdDS0pLdSpGamoqtW7fWuVqimhAC+/btQ05OjqzgQ0R1Y4GBiIiIiKgFaTQaREZGwt/fH56enlL7tGnT4Ofnh5CQkBbMDnB3d4eTkxNmz56N27dvo7KyEjExMbh27RoKCwsfu19wcDA+/vhj5ObmQqPRICUlBQkJCXXu8zhJSUkwNjaGvr4+PvnkE6SkpKB169YAgKKiItjY2MjitbW1YWlpiaKiIgDAzZs3ER4ejvj4eJiamj72OGVlZTA2Noauri6GDh2KTz/9FC+++OIT50v0rGKBgYiIiIioBSmVSmRnZ2PLli1S286dO5GamooVK1a0XGL/n46ODhISEnDhwgVYWlrC0NAQaWlpGDx4MLS0Hn85sXLlSnTs2BHu7u7Q1dVFREQEJkyYUOc+jzNw4ECoVCocOnQIgwYNwujRo/Hrr782eP/Jkydj7Nix9a5GMDExgUqlwrFjx7B48WJMnz4d6enpT5wv0bOKBQYiIiIiohYSERGBpKQkpKWlwdHRUWpPTU3FpUuXYG5uDm1tbWhrawN4+AyDAQMGNHuePXv2hEqlQmlpKQoLC5GcnIybN2+iffv2j93H2toa27dvR3l5OfLy8nD+/HkYGxvXuc/jGBkZwdXVFT4+Pli3bh20tbWxbt06AICdnV2NYsODBw9w69Yt6daS1NRULF++XJrLiRMnoqysDNra2li/fr20n5aWFlxdXdGtWzfMmDED//d//4eoqKgnzpfoWaXd0gkQERERET1rhBCYMmUKEhMTkZ6ejnbt2sn6//73v9d4eGKXLl3wySefYNiwYc2ZqoyZmRkAIDc3F8ePH2/Qww/19fXRpk0bqNVqfPfddxg9enSj89BoNNKzIXx9fVFaWooTJ06gZ8+eAB4WFDQaDby9vQE8fE5DVVWVtP+OHTsQExODQ4cOoU2bNg06DhHVjwUGIiIiIqJmplQqsXnzZuzYsQMmJibSswLMzMxgYGAAOzu7Wh/s6OTkVKMY0Vh37tzBxYsXpc9XrlyBSqWCpaUlnJycAABbt26FtbU1nJyckJWVhalTpyI0NBRBQUHSfmFhYWjTpo30i/+RI0dQUFCAbt26oaCgAAsWLIBGo8H777/f4GOXl5dj8eLFGD58OOzt7VFSUoLY2FgUFBTg5ZdfBgB4eHhg0KBBmDx5Mj7//HOo1WpERETg1Vdfld4g4eHhITvn48ePQ0tLS/bMi6ioKPTq1QsdOnTA/fv3sXv3bnz11VdYvXp1U0010V8eCwxERERERM2s+qL1j7c7xMXFITw8vFlzOX78OAYOHCh9nj59OgBg/PjxiI+PBwAUFhZi+vTpKC4uhr29PcLCwjBv3jzZOPn5+bLnK9y7dw9z587F5cuXYWxsjCFDhuCrr76Cubl5g4/dqlUrnD9/Hhs2bEBJSQmsrKzQu3dv/PTTT9IrMwFg06ZNiIiIQEBAALS0tDBq1CisWrXqieahvLwc7777Lq5duwYDAwO4u7tj48aNeOWVV55oHKJnmUIIIVo6CSIiIiIiIiL638aHPBIRERERERFRo7HAQERERERERESNxgIDERERERERETUaCwxERERERERE1GgsMBARERERERFRo7HAQERERETUzKKiotC7d2+YmJjAxsYGoaGhyMnJkcUMGDAACoVCtr399tstksu9e/egVCphZWUFY2NjjBo1CsXFxXWOu2DBAri7u8PIyAgWFhYIDAzEkSNHZDEuLi41zjE6OrrW8S5evAgTExPZay4BID4+vsYY+vr6spiEhAQEBQXBysoKCoUCKpWqxvjNNd9Ef2UsMBARERERNbOMjAwolUocPnwYKSkpUKvVCAoKQnl5uSxu8uTJKCwslLalS5e2SC7Tpk3Drl27sHXrVmRkZOD69esYOXJkneN26tQJn332GbKysnDgwAG4uLggKCgIN27ckMUtXLhQdo5TpkypMZZarcaYMWPw/PPP13osU1NT2Rh5eXmy/vLycvTt2xcxMTF15twc8030V6bd0gkQERERET1rkpOTZZ/j4+NhY2ODEydOoF+/flK7oaEh7OzsWjSXsrIyrFu3Dps3b8YLL7wAAIiLi4OHhwcOHz4MHx+fWscdO3as7PPHH3+MdevW4fTp0wgICJDaTUxM6j3HuXPnwt3dHQEBATh06FCNfoVCUecY48aNAwBcvXq1zuM0x3wT/ZVxBQMRERERUQsrKysDAFhaWsraN23ahNatW8PT0xOzZ89GRUVFs+dy4sQJqNVqBAYGSjHu7u5wcnJCZmZmg8asrKzEmjVrYGZmhq5du8r6oqOjYWVlhe7du2PZsmV48OCBrD81NRVbt25FbGzsY8e/c+cOnJ2d0bZtW4SEhODMmTMNyuuPWmK+if5KuIKBiIiIiKgFaTQaREZGwt/fH56enlL72LFj4ezsDAcHB5w+fRqzZs1CTk4OEhISmjWXoqIi6Orq1nj2ga2tLYqKiuocLykpCa+++ioqKipgb2+PlJQUtG7dWup/77330KNHD1haWuLQoUOYPXs2CgsL8fHHHwMAbt68ifDwcGzcuBGmpqa1HsPNzQ3r16+Hl5cXysrKsHz5cvj5+eHMmTNwdHRs8Lm3xHwT/dWwwEBERERE1IKUSiWys7Nx4MABWfubb74p/btLly6wt7dHQEAALl26hA4dOjRrLn/WwIEDoVKpUFJSgrVr12L06NE4cuQIbGxsAADTp0+XYr28vKCrq4u33noLUVFR0NPTw+TJkzF27FjZbSN/5OvrC19fX+mzn58fPDw88MUXX2DRokUNzrUl5pvor4a3SBARERERtZCIiAgkJSUhLS2t3l/bvb29ATx8m0Jz5mJnZ4fKykqUlpbK4ouLi+t9XoGRkRFcXV3h4+ODdevWQVtbG+vWrXtsvLe3Nx48eCA9KyE1NRXLly+HtrY2tLW1MXHiRJSVlUFbWxvr16+vdQwdHR1079690fP0tOeb6K+IBQYiIiIiomYmhEBERAQSExORmpqKdu3a1btP9asV7e3tmzWXnj17QkdHB/v27ZPacnJykJ+fL1s50BAajQb3799/bL9KpYKWlpa0wiEzMxMqlUraFi5cCBMTE6hUKowYMaLWMaqqqpCVldXoeXpa8030V8ZbJIiIiIiImplSqcTmzZuxY8cOmJiYSM8yMDMzg4GBAS5duoTNmzdjyJAhsLKywunTpzFt2jT069cPXl5ezZqLmZkZJk6ciOnTp8PS0hKmpqaYMmUKfH19ZW+QcHd3R1RUFEaMGIHy8nIsXrwYw4cPh729PUpKShAbG4uCggK8/PLLAB4WD44cOYKBAwfCxMQEmZmZmDZtGl5//XVYWFgAADw8PGS5Hj9+HFpaWrJnVSxcuBA+Pj5wdXVFaWkpli1bhry8PEyaNEmKuXXrFvLz83H9+nUADwskwMPVGXZ2ds0630R/aYKIiIiIiJoVgFq3uLg4IYQQ+fn5ol+/fsLS0lLo6ekJV1dXMXPmTFFWVtbsuQghxN27d8W7774rLCwshKGhoRgxYoQoLCysMU71Pnfv3hUjRowQDg4OQldXV9jb24vhw4eLo0ePSvEnTpwQ3t7ewszMTOjr6wsPDw+xZMkSce/evcfmGhcXJ8zMzGRtkZGRwsnJSejq6gpbW1sxZMgQcfLkyRr71XaO8+fPF0I073wT/ZUphBCiuYsaRERERERERPTXwmcwEBEREREREVGjscBARERERERERI3GAgMRERERERERNRoLDERERERERETUaCwwEBEREREREVGjscBARERERNTMoqKi0Lt3b5iYmMDGxgahoaHIycmpEZeZmYkXXngBRkZGMDU1Rb9+/XD37t0mzWXt2rV4/vnnYWFhAQsLCwQGBuLo0aOyGCEEPvjgA9jb28PAwACBgYHIzc2tc1wXFxcoFIoam1KplGIGDBhQo//tt9+WjVPbGFu2bJHFbNq0CV27doWhoSHs7e3xxhtv4ObNm1J/fHx8jTH09fVlYyxYsADu7u4wMjKS5uHIkSNPNJdEzzoWGIiIiIiImllGRgaUSiUOHz6MlJQUqNVqBAUFoby8XIrJzMzEoEGDEBQUhKNHj+LYsWOIiIiAllbT/i98eno6xowZg7S0NGRmZqJt27YICgpCQUGBFLN06VKsWrUKn3/+OY4cOQIjIyMEBwfj3r17jx332LFjKCwslLaUlBQAwMsvvyyLmzx5sixu6dKlNcaKi4uTxYSGhkp9Bw8eRFhYGCZOnIgzZ85g69atOHr0KCZPniwbw9TUVDZGXl6erL9Tp0747LPPkJWVhQMHDsDFxQVBQUG4ceNGg+eS6FmnEEKIlk6CiIiIiOhZduPGDdjY2CAjIwP9+vUDAPj4+ODFF1/EokWLmjWXqqoqWFhY4LPPPkNYWBiEEHBwcMCMGTPwt7/9DQBQVlYGW1tbxMfH49VXX23QuJGRkUhKSkJubi4UCgWAhysYunXrhhUrVjx2P4VCgcTERFlR4VHLly/H6tWrcenSJant008/RUxMDK5duwbg4QqGyMhIlJaWNihXAPjtt99gZmaGvXv3IiAgoMH7ET3LuIKBiIiIiKiFlZWVAQAsLS0BAL/++iuOHDkCGxsb+Pn5wdbWFv3798eBAweeei4VFRVQq9VSLleuXEFRURECAwOlGDMzM3h7eyMzM7NBY1ZWVmLjxo144403pOJCtU2bNqF169bw9PTE7NmzUVFRUWN/pVKJ1q1bo0+fPli/fj0e/Y3U19cXv/zyC3bv3g0hBIqLi7Ft2zYMGTJENsadO3fg7OyMtm3bIiQkBGfOnKkz3zVr1sDMzAxdu3Zt0DkSEaDd0gkQERERET3LNBoNIiMj4e/vD09PTwDA5cuXATx8LsDy5cvRrVs3fPnllwgICEB2djY6duz41PKZNWsWHBwcpIJCUVERAMDW1lYWZ2trK/XVZ/v27SgtLUV4eLisfezYsXB2doaDgwNOnz6NWbNmIScnBwkJCVLMwoUL8cILL8DQ0BA//vgj3n33Xdy5cwfvvfceAMDf3x+bNm3CK6+8gnv37uHBgwcYNmwYYmNjpTHc3Nywfv16eHl5oaysDMuXL4efnx/OnDkDR0dHKS4pKQmvvvoqKioqYG9vj5SUFLRu3brhk0f0jOMtEkRERERELeidd97BDz/8gAMHDkgXu4cOHYK/vz9mz56NJUuWSLFeXl4YOnQooqKinkou0dHRWLp0KdLT0+Hl5SXL5fr167C3t5diR48eDYVCgW+++abecYODg6Grq4tdu3bVGZeamoqAgABcvHgRHTp0qDXmgw8+QFxcHH755RcAwNmzZxEYGIhp06YhODgYhYWFmDlzJnr37o1169bVOoZarYaHhwfGjBkjuwWlvLwchYWFKCkpwdq1a5GamiqtJCGi+vEWCSIiIiKiFhIREYGkpCSkpaXJfkmvvpDv3LmzLN7DwwP5+flPJZfly5cjOjoaP/74o1RcAAA7OzsAQHFxsSy+uLhY6qtLXl4e9u7di0mTJtUb6+3tDQC4ePFinTHXrl3D/fv3ATx8I4e/vz9mzpwJLy8vBAcH41//+hfWr1+PwsLCWsfQ0dFB9+7daxzHyMgIrq6u8PHxwbp166Ctrf3YIgUR1cQCAxERERFRMxNCICIiAomJiUhNTUW7du1k/S4uLnBwcKjx6soLFy7A2dm5yfNZunQpFi1ahOTkZPTq1UvW165dO9jZ2WHfvn1S22+//YYjR47A19e33rHj4uJgY2ODoUOH1hurUqkAQLZSorYYCwsL6OnpAXj4zIg/vlmjVatWAIDHLdauqqpCVlZWnccBHt6+Ul3IIKL68RkMRERERETNTKlUYvPmzdixYwdMTEykZxmYmZnBwMAACoUCM2fOxPz589G1a1d069YNGzZswPnz57Ft27YmzSUmJgYffPABNm/eDBcXFykXY2NjGBsbQ6FQIDIyEv/85z/RsWNHtGvXDvPmzYODg4PszQ4BAQEYMWIEIiIipDaNRoO4uDiMHz8e2tryS49Lly5h8+bNGDJkCKysrHD69GlMmzYN/fr1k1ZQ7Nq1C8XFxfDx8YG+vj5SUlKwZMkS6W0WADBs2DBMnjwZq1evlm6RiIyMRJ8+feDg4ADg4XMcfHx84OrqitLSUixbtgx5eXnSqory8nIsXrwYw4cPh729PUpKShAbG4uCgoIar9UkojoIIiIiIiJqVgBq3eLi4mRxUVFRwtHRURgaGgpfX1/x008/NXkuzs7OteYyf/58KUaj0Yh58+YJW1tboaenJwICAkROTk6NcR7dRwgh9uzZIwDUiBVCiPz8fNGvXz9haWkp9PT0hKurq5g5c6YoKyuTYn744QfRrVs3YWxsLIyMjETXrl3F559/LqqqqmRjrVq1SnTu3FkYGBgIe3t78dprr4lr165J/ZGRkcLJyUno6uoKW1tbMWTIEHHy5Emp/+7du2LEiBHCwcFB6OrqCnt7ezF8+HBx9OjRPzOlRM8sPuSRiIiIiIiIiBqNz2AgIiIiIiIiokZjgYGIiIiIiIiIGo0FBiIiIiIiIiJqNBYYiIiIiIiIiKjRWGAgIiIiIiIiokZjgYGIiIiIqJlFRUWhd+/eMDExgY2NDUJDQ5GTkyP1X716FQqFotZt69atzZoLAKxZswYDBgyAqakpFAoFSktL6x13//79GDZsGBwcHKBQKLB9+/YaMeHh4TXOb9CgQVJ/enr6Y+fh2LFjAIB79+4hPDwcXbp0gba2NkJDQ2vNJzY2Fh4eHjAwMICbmxu+/PJLWf+ZM2cwatQouLi4QKFQYMWKFfWeIxHJscBARERERNTMMjIyoFQqcfjwYaSkpECtViMoKAjl5eUAgLZt26KwsFC2ffjhhzA2NsbgwYObNRcAqKiowKBBg/CPf/yjweOWl5eja9euiI2NrTNu0KBBsvP8+uuvpT4/P78a8zBp0iS0a9cOvXr1AgBUVVXBwMAA7733HgIDA2s9xurVqzF79mwsWLAAZ86cwYcffgilUoldu3bJzrF9+/aIjo6GnZ1dg8+TiP5DIYQQLZ0EEREREdGz7MaNG7CxsUFGRgb69etXa0z37t3Ro0cPrFu3rsVySU9Px8CBA3H79m2Ym5s3eEyFQoHExMQaqwvCw8NRWlpa6+qG2qjVarRp0wZTpkzBvHnzavQ/bjw/Pz/4+/tj2bJlUtuMGTNw5MgRHDhwoMY4Li4uiIyMRGRkZIPyIqKHuIKBiIiIiKiFlZWVAQAsLS1r7T9x4gRUKhUmTpzY4rk0tfT0dNjY2MDNzQ3vvPMObt68+djYnTt34ubNm5gwYcITHeP+/fvQ19eXtRkYGODo0aNQq9V/Km8iqokFBiIiIiKiFqTRaBAZGQl/f394enrWGrNu3Tp4eHjAz8+vxXNpSoMGDcKXX36Jffv2ISYmBhkZGRg8eDCqqqpqjV+3bh2Cg4Ph6Oj4RMcJDg7Gv//9b5w4cQJCCBw/fhz//ve/oVarUVJS0hSnQkQAtFs6ASIiIiKiZ5lSqUR2dnatS/UB4O7du9i8eXOttwQ0dy5N7dVXX5X+3aVLF3h5eaFDhw5IT09HQECALPbatWvYs2cPvv322yc+zrx581BUVAQfHx8IIWBra4vx48dj6dKl0NLib65ETYX/NRERERERtZCIiAgkJSUhLS3tsb/Kb9u2DRUVFQgLC2vxXJ629u3bo3Xr1rh48WKNvri4OFhZWWH48OFPPK6BgQHWr1+PiooKXL16Ffn5+XBxcYGJiQmsra2bInUiAlcwEBERERE1OyEEpkyZgsTERKSnp6Ndu3aPjV23bh2GDx/+1C6EnySXp+3atWu4efMm7O3tZe1CCMTFxSEsLAw6Ojp/enwdHR2peLJlyxa89NJLXMFA1IRYYCAiIiIiamZKpRKbN2/Gjh07YGJigqKiIgCAmZkZDAwMpLiLFy9i//792L17d4vmUlRUhKKiImllQVZWFkxMTODk5CQ9DDIgIAAjRoxAREQEAODOnTuylQhXrlyBSqWCpaUlnJyccOfOHXz44YcYNWoU7OzscOnSJbz//vtwdXVFcHCwLMfU1FRcuXIFkyZNqvUczp49i8rKSty6dQu///47VCoVAKBbt24AgAsXLuDo0aPw9vbG7du38fHHHyM7OxsbNmyQxqisrMTZs2elfxcUFEClUsHY2Biurq6NmWKiZwZfU0lERERE1MwUCkWt7XFxcQgPD5c+/+Mf/8DGjRtx9erVp/ZLe0NyWbBgAT788MM6Y1xcXBAeHo4FCxYA+M8rLf9o/PjxiI+Px927dxEaGopTp06htLQUDg4OCAoKwqJFi2BrayvbZ+zYscjLy8PBgwdrzdXFxQV5eXk12qsvdc6dO4exY8ciJycHOjo6GDhwIGJiYuDm5ibFXr16tdbVG/3790d6enqtxyUiORYYiIiIiIiIiKjReMMRERERERERETUaCwxERERERERE1GgsMBARERERERFRo7HAQERERERERESNxgIDERERERERETUaCwxERERERM0sKioKvXv3homJCWxsbBAaGoqcnBxZTFFREcaNGwc7OzsYGRmhR48e+O6775o8l7Vr1+L555+HhYUFLCwsEBgYiKNHj8piEhISEBQUBCsrKygUCqhUqnrHjY+Ph0KhkG36+vqymOLiYoSHh8PBwQGGhoYYNGgQcnNzZTENnYfvv/8e3t7eMDAwgIWFBUJDQ2vNycvLC/r6+rCxsYFSqZT60tPTERISAnt7exgZGaFbt27YtGlTvedJRP/BAgMRERERUTPLyMiAUqnE4cOHkZKSArVajaCgIJSXl0sxYWFhyMnJwc6dO5GVlYWRI0di9OjROHXqVJPmkp6ejjFjxiAtLQ2ZmZlo27YtgoKCUFBQIMWUl5ejb9++iImJeaKxTU1NUVhYKG15eXlSnxACoaGhuHz5Mnbs2IFTp07B2dkZgYGBTzwP3333HcaNG4cJEybg559/xsGDBzF27FhZLh9//DHmzJmDv//97zhz5gz27t2L4OBgqf/QoUPw8vLCd999h9OnT2PChAkICwtDUlLSE50z0bNMIYQQLZ0EEREREdGz7MaNG7CxsUFGRgb69esHADA2Nsbq1asxbtw4Kc7KygoxMTGYNGnSU8ulqqoKFhYW+OyzzxAWFibru3r1Ktq1a4dTp06hW7dudY4THx+PyMhIlJaW1tp/4cIFuLm5ITs7G8899xwAQKPRwM7ODkuWLJHOsb55ePDgAVxcXPDhhx9i4sSJtR7r9u3baNOmDXbt2oWAgIAGzgQwdOhQ2NraYv369Q3eh+hZxhUMREREREQtrKysDABgaWkptfn5+eGbb77BrVu3oNFosGXLFty7dw8DBgx4qrlUVFRArVbLcvmz7ty5A2dnZ7Rt2xYhISE4c+aM1Hf//n0AkN02oaWlBT09PRw4cEBqq28eTp48iYKCAmhpaaF79+6wt7fH4MGDkZ2dLY2RkpICjUaDgoICeHh4wNHREaNHj8Yvv/xSZ/5lZWVNMg9EzwoWGIiIiIiIWpBGo0FkZCT8/f3h6ekptX/77bdQq9WwsrKCnp4e3nrrLSQmJsLV1fWp5jNr1iw4ODggMDCwUeO4ublh/fr12LFjBzZu3AiNRgM/Pz9cu3YNAODu7g4nJyfMnj0bt2/fRmVlJWJiYnDt2jUUFhZK49Q3D5cvXwYALFiwAHPnzkVSUhIsLCwwYMAA3Lp1S4rRaDRYsmQJVqxYgW3btuHWrVt48cUXUVlZWWv+3377LY4dO4YJEyY0ah6IniUsMBARERERtSClUons7Gxs2bJF1j5v3jyUlpZi7969OH78OKZPn47Ro0cjKyvrqeUSHR2NLVu2IDExscYDGZ+Ur68vwsLC0K1bN/Tv3x8JCQmwtrbGF198AQDQ0dFBQkICLly4AEtLSxgaGiItLQ2DBw+GltZ/LlPqmweNRgMAmDNnDkaNGoWePXsiLi4OCoUCW7dulWLUajVWrVqF4OBg+Pj44Ouvv0Zubi7S0tJq5J6WloYJEyZg7dq10u0bRFQ/7ZZOgIiIiIjoWRUREYGkpCTs378fjo6OUvulS5fw2WefyZ5P0LVrV/z000+IjY3F559/3uS5LF++HNHR0di7dy+8vLyafHwdHR10794dFy9elNp69uwJlUqFsrIyVFZWwtraGt7e3ujVqxeAhs2Dvb09AKBz587SuHp6emjfvj3y8/MBoNYYa2trtG7dWoqplpGRgWHDhuGTTz6p8QwKIqobVzAQERERETUzIQQiIiKQmJiI1NRUtGvXTtZfUVEBALJf8gGgVatW0i/2TWnp0qVYtGgRkpOTpYv7plZVVYWsrCzpYv9RZmZmsLa2Rm5uLo4fP46QkBAADZuHnj17Qk9PT/aaT7VajatXr8LZ2RkA4O/vDwCymFu3bqGkpESKAR6+UWPo0KGIiYnBm2++2RSnTfRM4QoGIiIiIqJmplQqsXnzZuzYsQMmJiYoKioC8PBC28DAAO7u7nB1dcVbb72F5cuXw8rKCtu3b0dKSkqTvzYxJiYGH3zwATZv3gwXFxcpF2NjYxgbGwN4eDGen5+P69evA/jPhbqdnR3s7OwAPHydZJs2bRAVFQUAWLhwIXx8fODq6orS0lIsW7YMeXl5sjdgbN26FdbW1nByckJWVhamTp2K0NBQBAUFAUCD5sHU1BRvv/025s+fj7Zt28LZ2RnLli0DALz88ssAgE6dOiEkJARTp07FmjVrYGpqitmzZ8Pd3R0DBw4E8PC2iJdeeglTp07FqFGjpHnQ1dXlgx6JGkoQEREREVGzAlDrFhcXJ8VcuHBBjBw5UtjY2AhDQ0Ph5eUlvvzyyybPxdnZudZc5s+fL8XExcXVG9O/f38xfvx46XNkZKRwcnISurq6wtbWVgwZMkScPHlSduyVK1cKR0dHoaOjI5ycnMTcuXPF/fv3ZTENmYfKykoxY8YMYWNjI0xMTERgYKDIzs6WxZSVlYk33nhDmJubC0tLSzFixAiRn58v9Y8fP77Wc+zfv/+fm1iiZ5BCCCGataJBRERERERERH85fAYDERERERERETUaCwxERERERERE1GgsMBARERERERFRo7HAQERERERERESNxgIDERERERERETUaCwxERERERERE1GgsMBARERERNbOoqCj07t0bJiYmsLGxQWhoKHJycmQxly5dwogRI2BtbQ1TU1OMHj0axcXFTZ7L2rVr8fzzz8PCwgIWFhYIDAzE0aNHpX61Wo1Zs2ahS5cuMDIygoODA8LCwnD9+vV6x46NjYWLiwv09fXh7e0tGxcA7t27B6VSCSsrKxgbG2PUqFE1zjE/Px9Dhw6FoaEhbGxsMHPmTDx48EAWk56ejh49ekBPTw+urq6Ij49/KrkQUd1YYCAiIiIiamYZGRlQKpU4fPgwUlJSoFarERQUhPLycgBAeXk5goKCoFAokJqaioMHD6KyshLDhg2DRqNp0lzS09MxZswYpKWlITMzE23btkVQUBAKCgoAABUVFTh58iTmzZuHkydPIiEhATk5ORg+fHid437zzTeYPn065s+fj5MnT6Jr164IDg7Gr7/+KsVMmzYNu3btwtatW5GRkYHr169j5MiRUn9VVRWGDh2KyspKHDp0CBs2bEB8fDw++OADKebKlSsYOnQoBg4cCJVKhcjISEyaNAl79uxp0lyIqAEEERERERG1qF9//VUAEBkZGUIIIfbs2SO0tLREWVmZFFNaWioUCoVISUl5qrk8ePBAmJiYiA0bNjw25ujRowKAyMvLe2xMnz59hFKplD5XVVUJBwcHERUVJYR4eD46Ojpi69atUsy5c+cEAJGZmSmEEGL37t1CS0tLFBUVSTGrV68Wpqam4v79+0IIId5//33x3HPPyY79yiuviODg4CbNhYjqxxUMREREREQtrKysDABgaWkJALh//z4UCgX09PSkGH19fWhpaeHAgQNPNZeKigqo1Wopl8flq1AoYG5uXmt/ZWUlTpw4gcDAQKlNS0sLgYGByMzMBACcOHECarVaFuPu7g4nJycpJjMzE126dIGtra0UExwcjN9++w1nzpyRYh4dozqmeoymyoWI6scCAxERERFRC9JoNIiMjIS/vz88PT0BAD4+PjAyMsKsWbNQUVGB8vJy/O1vf0NVVRUKCwufaj6zZs2Cg4NDjYv2avfu3cOsWbMwZswYmJqa1hpTUlKCqqoqWWEAAGxtbVFUVAQAKCoqgq6ubo0ixR9jahujuq+umN9++w13795tslyIqH4sMBARERERtSClUons7Gxs2bJFarO2tsbWrVuxa9cuGBsbw8zMDKWlpejRowe0tJ7e/8JHR0djy5YtSExMhL6+fo1+tVqN0aNHQwiB1atXP7U8iOh/k3ZLJ0BERERE9KyKiIhAUlIS9u/fD0dHR1lfUFAQLl26hJKSEmhra8Pc3Bx2dnZo3779U8ll+fLliI6Oxt69e+Hl5VWjv7q4kJeXh9TU1MeuXgCA1q1bo1WrVjXewlBcXAw7OzsAgJ2dHSorK1FaWipbOfDHmD++7aF6zEdjajuOqakpDAwM0KpVqybJhYjqxxUMRERERETNTAiBiIgIJCYmIjU1Fe3atXtsbOvWrWFubo7U1FT8+uuv9b694c9YunQpFi1ahOTkZPTq1atGf3VxITc3F3v37oWVlVWd4+nq6qJnz57Yt2+f1KbRaLBv3z74+voCAHr27AkdHR1ZTE5ODvLz86UYX19fZGVlyd72kJKSAlNTU3Tu3FmKeXSM6pjqMZoqFyJqgJZ+yiQRERER0bPmnXfeEWZmZiI9PV0UFhZKW0VFhRSzfv16kZmZKS5evCi++uorYWlpKaZPn97kuURHRwtdXV2xbds2WS6///67EEKIyspKMXz4cOHo6ChUKpUspvpNDkII8cILL4hPP/1U+rxlyxahp6cn4uPjxdmzZ8Wbb74pzM3NZW+EePvtt4WTk5NITU0Vx48fF76+vsLX11fqf/DggfD09BRBQUFCpVKJ5ORkYW1tLWbPni3FXL58WRgaGoqZM2eKc+fOidjYWNGqVSuRnJzcpLkQUf1YYCAiIiIiamYAat3i4uKkmFmzZglbW1uho6MjOnbsKD766COh0WiaPBdnZ+dac5k/f74QQogrV648Nt+0tDTZONX7VPv000+Fk5OT0NXVFX369BGHDx+W9d+9e1e8++67wsLCQhgaGooRI0aIwsJCWczVq1fF4MGDhYGBgWjdurWYMWOGUKvVspi0tDTRrVs3oaurK9q3by+bx6bMhYjqphBCiGZfNkFEREREREREfyl8BgMRERERERERNRoLDERERERERETUaCwwEBEREREREVGjscBARERERERERI3GAgMRERER0X8JhUKB7du3t3QaCA8PR2hoaEunAQAYMGAAIiMjWzqNBhNC4M0334SlpSUUCgVUKtX/3DkQ/VksMBARERHRf43w8HAoFApER0fL2rdv3w6FQtFCWT256vP44zZo0KCWTk3m6tWr0kXwo1auXIn4+PgWyak+Li4uWLFiRUun8VjJycmIj49HUlISCgsL4enp+VSOU1fRIj4+Hl5eXtDX14eNjQ2USmWtcRcvXoSJiQnMzc2fSo707NFu6QSIiIiIiB6lr6+PmJgYvPXWW7CwsGjpdP60QYMGIS4uTtamp6fXQtk8GTMzs5ZO4X/WpUuXYG9vDz8/vxY5/scff4yPPvoIy5Ytg7e3N8rLy3H16tUacWq1GmPGjMHzzz+PQ4cONX+i9JfEFQxERERE9F8lMDAQdnZ2iIqKemzMzZs3MWbMGLRp0waGhobo0qULvv76a1nMgAEDMGXKFERGRsLCwgK2trZYu3YtysvLMWHCBJiYmMDV1RU//PCDbL/s7GwMHjwYxsbGsLW1xbhx41BSUvLE56Gnpwc7OzvZ9mjBJDc3F/369YO+vj46d+6MlJQU2f7p6elQKBQoLS2V2lQqFRQKheyC8eDBgxgwYAAMDQ1hYWGB4OBg3L59G8DDX9P79u0Lc3NzWFlZ4aWXXsKlS5ekfdu1awcA6N69OxQKBQYMGACg5i0S9+/fx3vvvQcbGxvo6+ujb9++OHbsWI1c9+3bh169esHQ0BB+fn7Iycl5ojkrLy9HWFgYjI2NYW9vj48++kjWP2DAAOTl5WHatGnSqpDy8nKYmppi27Ztstjt27fDyMgIv//+u7RSY8uWLfDz84O+vj48PT2RkZEh26ex3314eDimTJmC/Px8KBQKuLi41Bp3+/ZthIWFwcLCAoaGhhg8eDByc3Ol/vr+vsPDw5GRkYGVK1dK83D16lXcvn0bc+fOxZdffomxY8eiQ4cO8PLywvDhw2vkMHfuXLi7u2P06NENPj+i+rDAQERERET/VVq1aoUlS5bg008/xbVr12qNuXfvHnr27Invv/8e2dnZePPNNzFu3DgcPXpUFrdhwwa0bt0aR48exZQpU/DOO+/g5Zdfhp+fH06ePImgoCCMGzcOFRUVAIDS0lK88MIL6N69O44fP47k5GQUFxfLLsLi4+MbfbuGRqPByJEjoauriyNHjuDzzz/HrFmznngclUqFgIAAdO7cGZmZmThw4ACGDRuGqqoqAA8v2KdPn47jx49j37590NLSwogRI6DRaABAmq+9e/eisLAQCQkJtR7n/fffx3fffYcNGzbg5MmTcHV1RXBwMG7duiWLmzNnDj766CMcP34c2traeOONN57ofGbOnImMjAzs2LEDP/74I9LT03Hy5EmpPyEhAY6Ojli4cCEKCwtRWFgIIyMjvPrqqzVWi8TFxeH//u//YGJiIht/xowZOHXqFHx9fTFs2DDcvHkTQNN89ytXrsTChQvh6OiIwsJCWRHmUeHh4Th+/Dh27tyJzMxMCCEwZMgQqNVqAPX/fa9cuRK+vr6YPHmyNA9t27ZFSkoKNBoNCgoK4OHhAUdHR4wePRq//PKL7PipqanYunUrYmNjG/K1EDWcICIiIiL6LzF+/HgREhIihBDCx8dHvPHGG0IIIRITE0V9/+s6dOhQMWPGDOlz//79Rd++faXPDx48EEZGRmLcuHFSW2FhoQAgMjMzhRBCLFq0SAQFBcnG/eWXXwQAkZOTI4QQIiEhQbi5udV7Hq1atRJGRkaybfHixUIIIfbs2SO0tbVFQUGBtM8PP/wgAIjExEQhhBBpaWkCgLh9+7YUc+rUKQFAXLlyRQghxJgxY4S/v3+duTzqxo0bAoDIysoSQghx5coVAUCcOnWqRv7V38OdO3eEjo6O2LRpk9RfWVkpHBwcxNKlS2W57t27V4r5/vvvBQBx9+7dBuX2+++/C11dXfHtt99KbTdv3hQGBgZi6tSpUpuzs7P45JNPZPseOXJEtGrVSly/fl0IIURxcbHQ1tYW6enpsvOMjo6W9lGr1cLR0VHExMQIIZruu//kk0+Es7OzrK1///7SOVy4cEEAEAcPHpT6S0pKhIGBgezc/6i2v+9H50UIIaKiooSOjo5wc3MTycnJIjMzUwQEBAg3Nzdx//596Vht27YVGRkZQggh4uLihJmZWZ3nRNRQXMFARERERP+VYmJisGHDBpw7d65GX1VVFRYtWoQuXbrA0tISxsbG2LNnD/Lz82VxXl5e0r9btWoFKysrdOnSRWqztbUFAPz6668AgJ9//hlpaWkwNjaWNnd3dwCQbi0YMWIEzp8/X2/+AwcOhEqlkm1vv/02AODcuXNo27YtHBwcpHhfX98GzcujqlcwPE5ubi7GjBmD9u3bw9TUVFqy/8d5qsulS5egVqvh7+8vteno6KBPnz41vptH59ve3h7Af+a2IceprKyEt7e31GZpaQk3N7d69+3Tpw+ee+45bNiwAQCwceNGODs7o1+/frK4R+dYW1sbvXr1ks6hKb/7upw7dw7a2tqy87SysoKbm5uUS0P/vv9Io9FArVZj1apVCA4Oho+PD77++mvk5uYiLS0NADB58mSMHTu2xtwQNQU+5JGIiIiI/iv169cPwcHBmD17NsLDw2V9y5Ytw8qVK7FixQp06dIFRkZGiIyMRGVlpSxOR0dH9lmhUMjaqpe7V98ycOfOHQwbNgwxMTE18qm+YG4oIyMjuLq6PtE+j9LSevhboBBCaqteQl/NwMCgzjGGDRsGZ2dnrF27Fg4ODtBoNPD09KwxT02lrrl92iZNmoTY2Fj8/e9/R1xcHCZMmPBEt7I05XffWA39+/6j6jw7d+4stVlbW6N169ZScSI1NRU7d+7E8uXLATz8+9JoNNDW1saaNWue+LYWokdxBQMRERER/deKjo7Grl27kJmZKWs/ePAgQkJC8Prrr6Nr165o3749Lly40Ojj9ejRA2fOnIGLiwtcXV1lm5GRUaPHr+bh4YFffvkFhYWFUtvhw4dlMdbW1gAgi/nj6yS9vLywb9++Wo9x8+ZN5OTkYO7cuQgICICHh4f08Mdqurq6ACA9s6E2HTp0gK6uLg4ePCi1qdVqHDt2THYh21gdOnSAjo4Ojhw5IrXdvn27xveqq6tba76vv/468vLysGrVKpw9exbjx4+vEfPoHD948AAnTpyAh4cHgOb97h88eCA7z+rvqno+G/L3Xds8VK8yefThmrdu3UJJSQmcnZ0BAJmZmbJVNQsXLoSJiQlUKhVGjBjRZOdJzyYWGIiIiIjov1aXLl3w2muvYdWqVbL2jh07IiUlBYcOHcK5c+fw1ltvobi4uNHHUyqVuHXrFsaMGYNjx47h0qVL2LNnDyZMmCBdzCUmJkpL5+ty//59FBUVybbqNxIEBgaiU6dOGD9+PH7++Wf89NNPmDNnjmx/V1dXtG3bFgsWLEBubi6+//77Gm9VmD17No4dO4Z3330Xp0+fxvnz57F69WqUlJTAwsICVlZWWLNmDS5evIjU1FRMnz5dtr+NjQ0MDAykBxqWlZXVOA8jIyO88847mDlzJpKTk3H27FlMnjwZFRUVmDhx4hPNb12MjY0xceJEzJw5E6mpqcjOzkZ4eLi0kqOai4sL9u/fj4KCAtkbHiwsLDBy5EjMnDkTQUFBcHR0rHGM2NhYJCYm4vz581Aqlbh9+7b0i31Tfvd16dixI0JCQjB58mQcOHAAP//8M15//XW0adMGISEhUkx9f98uLi44cuQIrl69ipKSEmg0GnTq1AkhISGYOnUqDh06hOzsbIwfPx7u7u4YOHAggIcFDk9PT2lr06YNtLS04Onp+T/9Wlj678ACAxERERH9V1u4cGGNZfZz585Fjx49EBwcjAEDBsDOzk72WsU/y8HBAQcPHkRVVRWCgoLQpUsXREZGwtzcXLrQLSsra9DrF5OTk2Fvby/b+vbtC+Dh7Q+JiYm4e/cu+vTpg0mTJmHx4sWy/XV0dPD111/j/Pnz8PLyQkxMDP75z3/KYjp16oQff/wRP//8M/r06QNfX1/s2LED2tra0NLSwpYtW3DixAl4enpi2rRpWLZsmWx/bW1trFq1Cl988QUcHBykC9w/io6OxqhRozBu3Dj06NEDFy9exJ49e57ogrT6VZHp6emPjVm2bBmef/55DBs2DIGBgejbty969uwpi1m4cCGuXr2KDh06SKs8qk2cOBGVlZWPXeYfHR2N6OhodO3aFQcOHMDOnTvRunVrAE373dcnLi4OPXv2xEsvvQRfX18IIbB7927pFpOG/H3/7W9/Q6tWrdC5c2dYW1tLt0B8+eWX8Pb2xtChQ9G/f3/o6OggOTm5xu1CRE+DQjx6UxcREREREdFTkJaWhpEjR+Ly5ctP7Zfyr776CtOmTcP169el2z+Ah8WNdu3a4dSpU+jWrdtTOTYR8SGPRERERETUDHbv3o1//OMfT6W4UFFRgcLCQkRHR+Ott96SFReIqPnwFgkiIiIiInrqli1bhpkzZz6VsZcuXQp3d3fY2dlh9uzZT+UYRFQ/3iJBRERERERERI3GFQxERERERERE1GgsMBARERERERFRo7HAQERERERERESNxgIDERERERERETUaCwxERERERERE1GgsMBARERERERFRo7HAQERERERERESNxgIDERERERERETXa/wMAegnhsB9OBgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.Education, data.Income)\n",
    "plt.xlabel(data.Education)\n",
    "plt.ylabel(data.Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b002be40-9931-4dfe-b901-724451fee788",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wx+b\n",
    "w = torch.randn(1, requires_grad = True)\n",
    "b = torch.zeros(1, requires_grad = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2819bc38-7c95-44ca-9f86-3a2096b50e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.9357], requires_grad=True)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69c394d5-2c5b-4c01-9c39-ad8e3ef4c3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.8902180473716012"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7a32424c-114a-4f5b-89c2-d3679806f1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.float32"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c0b0e1cf-f805-4f23-beaf-a5d8548127b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.9357])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "941705b9-c688-42a5-a7ad-57c1cdcb2ac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#设置学习率\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "83b73d1c-86c2-416d-a56e-a34f9f9290fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_n = data.Education.values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6efdf5d4-e5c3-4e0b-8ada-f8c9dd641e14",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Numpy is not available",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[64], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_n\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Numpy is not available"
     ]
    }
   ],
   "source": [
    "X = torch.from_numpy(X_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94b60eab-f034-4f21-bcbf-eb38ab6e19a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#未完"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0714af8d-1c92-40c2-9bb8-79da09aa3e45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "78cbeefe-3330-40c9-b2ce-8f271bce570f",
   "metadata": {},
   "source": [
    "### 实现线性回归--封装写法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4f7030c8-f4dd-4a5d-96be-87b9cb0b9052",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b99741e7-9c30-4985-85a1-aa12d3dc392d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=1, out_features=1, bias=True)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wx + b\n",
    "model = nn.Linear(1,1)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cca6efaf-92df-473d-8d79-13298b3fa1e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义损失函数\n",
    "loss_fn = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "efa24e46-bd94-4d28-9b3c-38ef3ecd5ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Module.parameters at 0x00000210960B18C0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# w\n",
    "model.parameters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "23063b37-0c1e-4e40-beef-5517929f4a05",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'torch' has no attribute 'version'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[75], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# 定义优化器\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# 优化器的第一个参数必须是要更新的模型中的参数\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m opt \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSGD\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\optim\\sgd.py:27\u001b[0m, in \u001b[0;36mSGD.__init__\u001b[1;34m(self, params, lr, momentum, dampening, weight_decay, nesterov, maximize, foreach, differentiable, fused)\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m nesterov \u001b[38;5;129;01mand\u001b[39;00m (momentum \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m dampening \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m     26\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNesterov momentum requires a momentum and zero dampening\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 27\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdefaults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fused:\n\u001b[0;32m     30\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_step_supports_amp_scaling \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\optim\\optimizer.py:284\u001b[0m, in \u001b[0;36mOptimizer.__init__\u001b[1;34m(self, params, defaults)\u001b[0m\n\u001b[0;32m    281\u001b[0m     param_groups \u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparams\u001b[39m\u001b[38;5;124m'\u001b[39m: param_groups}]\n\u001b[0;32m    283\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m param_group \u001b[38;5;129;01min\u001b[39;00m param_groups:\n\u001b[1;32m--> 284\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_param_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam_group\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    286\u001b[0m \u001b[38;5;66;03m# Allows _cuda_graph_capture_health_check to rig a poor man's TORCH_WARN_ONCE in python,\u001b[39;00m\n\u001b[0;32m    287\u001b[0m \u001b[38;5;66;03m# which I don't think exists\u001b[39;00m\n\u001b[0;32m    288\u001b[0m \u001b[38;5;66;03m# https://github.com/pytorch/pytorch/issues/72948\u001b[39;00m\n\u001b[0;32m    289\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_warned_capturable_if_run_uncaptured \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\_compile.py:22\u001b[0m, in \u001b[0;36m_disable_dynamo.<locals>.inner\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fn)\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 22\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_dynamo\u001b[39;00m\n\u001b[0;32m     24\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mdisable(fn, recursive)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\_dynamo\\__init__.py:2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert_frame, eval_frame, resume_execution\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregistry\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m list_backends, lookup_backend, register_backend\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcallback\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m callback_handler, on_compile_end, on_compile_start\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\_dynamo\\convert_frame.py:40\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_python_dispatch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _disable_current_modes\n\u001b[0;32m     38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_traceback\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m format_traceback_short\n\u001b[1;32m---> 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config, exc, trace_rules\n\u001b[0;32m     41\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregistry\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CompilerFn\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbytecode_analysis\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m remove_dead_code, remove_pointless_jumps\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\_dynamo\\config.py:344\u001b[0m\n\u001b[0;32m    340\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mgetcwd(), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_compile_debug\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    343\u001b[0m \u001b[38;5;66;03m# [@compile_ignored: debug]\u001b[39;00m\n\u001b[1;32m--> 344\u001b[0m debug_dir_root \u001b[38;5;241m=\u001b[39m \u001b[43mdefault_debug_dir_root\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    346\u001b[0m \u001b[38;5;66;03m# [@compile_ignored: debug]\u001b[39;00m\n\u001b[0;32m    347\u001b[0m _save_config_ignore \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrepro_after\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    349\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrepro_level\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    353\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipfiles_inline_module_allowlist\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    354\u001b[0m }\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\_dynamo\\config.py:335\u001b[0m, in \u001b[0;36mdefault_debug_dir_root\u001b[1;34m()\u001b[0m\n\u001b[0;32m    333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m DEBUG_DIR_VAR_NAME \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39menviron:\n\u001b[0;32m    334\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39menviron[DEBUG_DIR_VAR_NAME], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_compile_debug\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 335\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[43mis_fbcode\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    336\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\n\u001b[0;32m    337\u001b[0m         tempfile\u001b[38;5;241m.\u001b[39mgettempdir(), getpass\u001b[38;5;241m.\u001b[39mgetuser(), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_compile_debug\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    338\u001b[0m     )\n\u001b[0;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\_dynamo\\config.py:327\u001b[0m, in \u001b[0;36mis_fbcode\u001b[1;34m()\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mis_fbcode\u001b[39m():\n\u001b[1;32m--> 327\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mversion\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgit_version\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mD:\\developer\\Python312\\Lib\\site-packages\\torch\\__init__.py:2005\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m   2002\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m\n\u001b[0;32m   2003\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m-> 2005\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'torch' has no attribute 'version'"
     ]
    }
   ],
   "source": [
    "# 定义优化器\n",
    "# 优化器的第一个参数必须是要更新的模型中的参数\n",
    "opt = torch.optim.SGD(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "24636462-5375-40fd-88e6-b11721fd1bff",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[72], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# 训练\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m5000\u001b[39m):\n\u001b[1;32m----> 3\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m x, y \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[43mX\u001b[49m, Y):\n\u001b[0;32m      4\u001b[0m         y_pred \u001b[38;5;241m=\u001b[39m model(x)\n\u001b[0;32m      5\u001b[0m         loss \u001b[38;5;241m=\u001b[39m loss_fn(y, y_pred)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "for epoch in range(5000):\n",
    "    for x, y in zip(X, Y):\n",
    "        y_pred = model(x)\n",
    "        loss = loss_fn(y, y_pred)\n",
    "        # 梯度清零操作\n",
    "        opt.zero_grad()\n",
    "        loss.backward()\n",
    "        # 更新操作\n",
    "        opt.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c674815-7514-4868-9140-821a9b5a88f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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